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| a | b/scripts/grad-cam & weights/Grad-cam.ipynb | ||
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| 1 | { |
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| 2 | "cells": [ |
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| 3 | { |
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| 4 | "cell_type": "markdown", |
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| 5 | "id": "dc054015", |
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| 6 | "metadata": {}, |
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| 7 | "source": [ |
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| 8 | "# Free GPU memory" |
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| 9 | ] |
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| 10 | }, |
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| 11 | { |
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| 12 | "cell_type": "code", |
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| 13 | "execution_count": 1, |
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| 14 | "id": "0c6ed669", |
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| 15 | "metadata": {}, |
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| 16 | "outputs": [], |
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| 17 | "source": [ |
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| 18 | "import tensorflow as tf \n", |
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| 19 | "physical_devices = tf.config.list_physical_devices('GPU') \n", |
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| 20 | "tf.config.experimental.set_memory_growth(physical_devices[0], True)" |
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| 21 | ] |
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| 22 | }, |
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| 23 | { |
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| 24 | "cell_type": "markdown", |
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| 25 | "id": "efba6c8b", |
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| 26 | "metadata": {}, |
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| 27 | "source": [ |
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| 28 | "# Grad-cam Implementation\n", |
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| 29 | "## Commented out codes are for other datasets and models used in this work. " |
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| 30 | ] |
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| 31 | }, |
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| 32 | { |
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| 33 | "cell_type": "code", |
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| 34 | "execution_count": 105, |
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| 35 | "id": "c87d97b1", |
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| 36 | "metadata": {}, |
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| 37 | "outputs": [ |
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| 38 | { |
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| 39 | "name": "stdout", |
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| 40 | "output_type": "stream", |
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| 41 | "text": [ |
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| 42 | "MAx: 195\n", |
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| 43 | "MIN: 26\n", |
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| 44 | "Cropped_img MAx: 188.0\n", |
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| 45 | "Cropped_img MIN: 26.0\n", |
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| 46 | "uint8\n", |
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| 47 | "Rescaled_cropped_img MAx: 0.7372549019607844\n", |
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| 48 | "Resclased_cropped_img MIN: 0.10196078431372549\n", |
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| 49 | "<class 'numpy.ndarray'>\n", |
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| 50 | "(150, 150, 3)\n", |
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| 51 | "float64\n", |
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| 52 | "float64 (1, 150, 150, 3)\n", |
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| 53 | "Model: \"model_12\"\n", |
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| 54 | "__________________________________________________________________________________________________\n", |
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| 55 | "Layer (type) Output Shape Param # Connected to \n", |
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| 56 | "==================================================================================================\n", |
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| 57 | "input_13 (InputLayer) [(None, 150, 150, 3) 0 \n", |
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| 58 | "__________________________________________________________________________________________________\n", |
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| 59 | "zero_padding2d_24 (ZeroPadding2 (None, 156, 156, 3) 0 input_13[0][0] \n", |
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| 60 | "__________________________________________________________________________________________________\n", |
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| 61 | "conv1/conv (Conv2D) (None, 75, 75, 64) 9408 zero_padding2d_24[0][0] \n", |
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| 62 | "__________________________________________________________________________________________________\n", |
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| 63 | "conv1/bn (BatchNormalization) (None, 75, 75, 64) 256 conv1/conv[0][0] \n", |
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| 64 | "__________________________________________________________________________________________________\n", |
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| 65 | "conv1/relu (Activation) (None, 75, 75, 64) 0 conv1/bn[0][0] \n", |
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| 66 | "__________________________________________________________________________________________________\n", |
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| 67 | "zero_padding2d_25 (ZeroPadding2 (None, 77, 77, 64) 0 conv1/relu[0][0] \n", |
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| 68 | "__________________________________________________________________________________________________\n", |
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| 69 | "pool1 (MaxPooling2D) (None, 38, 38, 64) 0 zero_padding2d_25[0][0] \n", |
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| 70 | "__________________________________________________________________________________________________\n", |
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| 71 | "conv2_block1_0_bn (BatchNormali (None, 38, 38, 64) 256 pool1[0][0] \n", |
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| 72 | "__________________________________________________________________________________________________\n", |
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| 73 | "conv2_block1_0_relu (Activation (None, 38, 38, 64) 0 conv2_block1_0_bn[0][0] \n", |
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| 74 | "__________________________________________________________________________________________________\n", |
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| 75 | "conv2_block1_1_conv (Conv2D) (None, 38, 38, 128) 8192 conv2_block1_0_relu[0][0] \n", |
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| 76 | "__________________________________________________________________________________________________\n", |
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| 77 | "conv2_block1_1_bn (BatchNormali (None, 38, 38, 128) 512 conv2_block1_1_conv[0][0] \n", |
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| 78 | "__________________________________________________________________________________________________\n", |
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| 79 | "conv2_block1_1_relu (Activation (None, 38, 38, 128) 0 conv2_block1_1_bn[0][0] \n", |
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| 80 | "__________________________________________________________________________________________________\n", |
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| 81 | "conv2_block1_2_conv (Conv2D) (None, 38, 38, 32) 36864 conv2_block1_1_relu[0][0] \n", |
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| 82 | "__________________________________________________________________________________________________\n", |
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| 83 | "conv2_block1_concat (Concatenat (None, 38, 38, 96) 0 pool1[0][0] \n", |
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| 84 | " conv2_block1_2_conv[0][0] \n", |
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| 85 | "__________________________________________________________________________________________________\n", |
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| 86 | "conv2_block2_0_bn (BatchNormali (None, 38, 38, 96) 384 conv2_block1_concat[0][0] \n", |
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| 87 | "__________________________________________________________________________________________________\n", |
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| 88 | "conv2_block2_0_relu (Activation (None, 38, 38, 96) 0 conv2_block2_0_bn[0][0] \n", |
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| 89 | "__________________________________________________________________________________________________\n", |
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| 90 | "conv2_block2_1_conv (Conv2D) (None, 38, 38, 128) 12288 conv2_block2_0_relu[0][0] \n", |
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| 91 | "__________________________________________________________________________________________________\n", |
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| 92 | "conv2_block2_1_bn (BatchNormali (None, 38, 38, 128) 512 conv2_block2_1_conv[0][0] \n", |
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| 93 | "__________________________________________________________________________________________________\n", |
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| 94 | "conv2_block2_1_relu (Activation (None, 38, 38, 128) 0 conv2_block2_1_bn[0][0] \n", |
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| 95 | "__________________________________________________________________________________________________\n", |
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| 96 | "conv2_block2_2_conv (Conv2D) (None, 38, 38, 32) 36864 conv2_block2_1_relu[0][0] \n", |
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| 97 | "__________________________________________________________________________________________________\n", |
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| 98 | "conv2_block2_concat (Concatenat (None, 38, 38, 128) 0 conv2_block1_concat[0][0] \n", |
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| 99 | " conv2_block2_2_conv[0][0] \n", |
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| 100 | "__________________________________________________________________________________________________\n", |
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| 101 | "conv2_block3_0_bn (BatchNormali (None, 38, 38, 128) 512 conv2_block2_concat[0][0] \n", |
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| 102 | "__________________________________________________________________________________________________\n", |
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| 103 | "conv2_block3_0_relu (Activation (None, 38, 38, 128) 0 conv2_block3_0_bn[0][0] \n", |
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| 104 | "__________________________________________________________________________________________________\n", |
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| 105 | "conv2_block3_1_conv (Conv2D) (None, 38, 38, 128) 16384 conv2_block3_0_relu[0][0] \n", |
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| 106 | "__________________________________________________________________________________________________\n", |
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| 107 | "conv2_block3_1_bn (BatchNormali (None, 38, 38, 128) 512 conv2_block3_1_conv[0][0] \n", |
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| 108 | "__________________________________________________________________________________________________\n", |
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| 109 | "conv2_block3_1_relu (Activation (None, 38, 38, 128) 0 conv2_block3_1_bn[0][0] \n", |
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| 110 | "__________________________________________________________________________________________________\n", |
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| 111 | "conv2_block3_2_conv (Conv2D) (None, 38, 38, 32) 36864 conv2_block3_1_relu[0][0] \n", |
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| 112 | "__________________________________________________________________________________________________\n", |
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| 113 | "conv2_block3_concat (Concatenat (None, 38, 38, 160) 0 conv2_block2_concat[0][0] \n", |
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| 114 | " conv2_block3_2_conv[0][0] \n", |
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| 115 | "__________________________________________________________________________________________________\n", |
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| 116 | "conv2_block4_0_bn (BatchNormali (None, 38, 38, 160) 640 conv2_block3_concat[0][0] \n", |
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| 117 | "__________________________________________________________________________________________________\n", |
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| 118 | "conv2_block4_0_relu (Activation (None, 38, 38, 160) 0 conv2_block4_0_bn[0][0] \n", |
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| 119 | "__________________________________________________________________________________________________\n", |
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| 120 | "conv2_block4_1_conv (Conv2D) (None, 38, 38, 128) 20480 conv2_block4_0_relu[0][0] \n", |
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| 121 | "__________________________________________________________________________________________________\n", |
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| 122 | "conv2_block4_1_bn (BatchNormali (None, 38, 38, 128) 512 conv2_block4_1_conv[0][0] \n", |
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| 123 | "__________________________________________________________________________________________________\n", |
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| 124 | "conv2_block4_1_relu (Activation (None, 38, 38, 128) 0 conv2_block4_1_bn[0][0] \n", |
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| 125 | "__________________________________________________________________________________________________\n", |
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| 126 | "conv2_block4_2_conv (Conv2D) (None, 38, 38, 32) 36864 conv2_block4_1_relu[0][0] \n", |
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| 127 | "__________________________________________________________________________________________________\n", |
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| 128 | "conv2_block4_concat (Concatenat (None, 38, 38, 192) 0 conv2_block3_concat[0][0] \n", |
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| 129 | " conv2_block4_2_conv[0][0] \n", |
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| 130 | "__________________________________________________________________________________________________\n", |
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| 131 | "conv2_block5_0_bn (BatchNormali (None, 38, 38, 192) 768 conv2_block4_concat[0][0] \n", |
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| 132 | "__________________________________________________________________________________________________\n", |
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| 133 | "conv2_block5_0_relu (Activation (None, 38, 38, 192) 0 conv2_block5_0_bn[0][0] \n", |
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| 134 | "__________________________________________________________________________________________________\n", |
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| 135 | "conv2_block5_1_conv (Conv2D) (None, 38, 38, 128) 24576 conv2_block5_0_relu[0][0] \n", |
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| 136 | "__________________________________________________________________________________________________\n", |
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| 137 | "conv2_block5_1_bn (BatchNormali (None, 38, 38, 128) 512 conv2_block5_1_conv[0][0] \n", |
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| 138 | "__________________________________________________________________________________________________\n", |
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| 139 | "conv2_block5_1_relu (Activation (None, 38, 38, 128) 0 conv2_block5_1_bn[0][0] \n", |
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| 140 | "__________________________________________________________________________________________________\n", |
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| 141 | "conv2_block5_2_conv (Conv2D) (None, 38, 38, 32) 36864 conv2_block5_1_relu[0][0] \n", |
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| 142 | "__________________________________________________________________________________________________\n", |
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| 143 | "conv2_block5_concat (Concatenat (None, 38, 38, 224) 0 conv2_block4_concat[0][0] \n", |
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| 144 | " conv2_block5_2_conv[0][0] \n", |
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| 145 | "__________________________________________________________________________________________________\n", |
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| 146 | "conv2_block6_0_bn (BatchNormali (None, 38, 38, 224) 896 conv2_block5_concat[0][0] \n", |
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| 147 | "__________________________________________________________________________________________________\n", |
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| 148 | "conv2_block6_0_relu (Activation (None, 38, 38, 224) 0 conv2_block6_0_bn[0][0] \n", |
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| 149 | "__________________________________________________________________________________________________\n", |
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| 150 | "conv2_block6_1_conv (Conv2D) (None, 38, 38, 128) 28672 conv2_block6_0_relu[0][0] \n", |
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| 151 | "__________________________________________________________________________________________________\n", |
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| 152 | "conv2_block6_1_bn (BatchNormali (None, 38, 38, 128) 512 conv2_block6_1_conv[0][0] \n", |
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| 153 | "__________________________________________________________________________________________________\n", |
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| 154 | "conv2_block6_1_relu (Activation (None, 38, 38, 128) 0 conv2_block6_1_bn[0][0] \n", |
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| 155 | "__________________________________________________________________________________________________\n", |
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| 156 | "conv2_block6_2_conv (Conv2D) (None, 38, 38, 32) 36864 conv2_block6_1_relu[0][0] \n", |
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| 157 | "__________________________________________________________________________________________________\n", |
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| 158 | "conv2_block6_concat (Concatenat (None, 38, 38, 256) 0 conv2_block5_concat[0][0] \n", |
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| 159 | " conv2_block6_2_conv[0][0] \n", |
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| 160 | "__________________________________________________________________________________________________\n", |
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| 161 | "pool2_bn (BatchNormalization) (None, 38, 38, 256) 1024 conv2_block6_concat[0][0] \n", |
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| 162 | "__________________________________________________________________________________________________\n", |
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| 163 | "pool2_relu (Activation) (None, 38, 38, 256) 0 pool2_bn[0][0] \n", |
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| 164 | "__________________________________________________________________________________________________\n", |
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| 165 | "pool2_conv (Conv2D) (None, 38, 38, 128) 32768 pool2_relu[0][0] \n", |
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| 166 | "__________________________________________________________________________________________________\n", |
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| 167 | "pool2_pool (AveragePooling2D) (None, 19, 19, 128) 0 pool2_conv[0][0] \n", |
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| 168 | "__________________________________________________________________________________________________\n", |
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| 169 | "conv3_block1_0_bn (BatchNormali (None, 19, 19, 128) 512 pool2_pool[0][0] \n", |
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| 170 | "__________________________________________________________________________________________________\n", |
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| 171 | "conv3_block1_0_relu (Activation (None, 19, 19, 128) 0 conv3_block1_0_bn[0][0] \n", |
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| 172 | "__________________________________________________________________________________________________\n", |
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| 173 | "conv3_block1_1_conv (Conv2D) (None, 19, 19, 128) 16384 conv3_block1_0_relu[0][0] \n", |
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| 174 | "__________________________________________________________________________________________________\n", |
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| 175 | "conv3_block1_1_bn (BatchNormali (None, 19, 19, 128) 512 conv3_block1_1_conv[0][0] \n", |
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| 176 | "__________________________________________________________________________________________________\n", |
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| 177 | "conv3_block1_1_relu (Activation (None, 19, 19, 128) 0 conv3_block1_1_bn[0][0] \n", |
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| 178 | "__________________________________________________________________________________________________\n", |
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| 179 | "conv3_block1_2_conv (Conv2D) (None, 19, 19, 32) 36864 conv3_block1_1_relu[0][0] \n", |
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| 180 | "__________________________________________________________________________________________________\n", |
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| 181 | "conv3_block1_concat (Concatenat (None, 19, 19, 160) 0 pool2_pool[0][0] \n", |
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| 182 | " conv3_block1_2_conv[0][0] \n", |
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| 183 | "__________________________________________________________________________________________________\n", |
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| 184 | "conv3_block2_0_bn (BatchNormali (None, 19, 19, 160) 640 conv3_block1_concat[0][0] \n", |
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| 185 | "__________________________________________________________________________________________________\n", |
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| 186 | "conv3_block2_0_relu (Activation (None, 19, 19, 160) 0 conv3_block2_0_bn[0][0] \n", |
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| 187 | "__________________________________________________________________________________________________\n", |
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| 188 | "conv3_block2_1_conv (Conv2D) (None, 19, 19, 128) 20480 conv3_block2_0_relu[0][0] \n", |
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| 189 | "__________________________________________________________________________________________________\n", |
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| 190 | "conv3_block2_1_bn (BatchNormali (None, 19, 19, 128) 512 conv3_block2_1_conv[0][0] \n", |
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| 191 | "__________________________________________________________________________________________________\n", |
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| 192 | "conv3_block2_1_relu (Activation (None, 19, 19, 128) 0 conv3_block2_1_bn[0][0] \n", |
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| 193 | "__________________________________________________________________________________________________\n", |
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| 194 | "conv3_block2_2_conv (Conv2D) (None, 19, 19, 32) 36864 conv3_block2_1_relu[0][0] \n", |
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| 195 | "__________________________________________________________________________________________________\n", |
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| 196 | "conv3_block2_concat (Concatenat (None, 19, 19, 192) 0 conv3_block1_concat[0][0] \n", |
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| 197 | " conv3_block2_2_conv[0][0] \n", |
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| 198 | "__________________________________________________________________________________________________\n", |
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| 199 | "conv3_block3_0_bn (BatchNormali (None, 19, 19, 192) 768 conv3_block2_concat[0][0] \n", |
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| 200 | "__________________________________________________________________________________________________\n", |
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| 201 | "conv3_block3_0_relu (Activation (None, 19, 19, 192) 0 conv3_block3_0_bn[0][0] \n", |
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| 202 | "__________________________________________________________________________________________________\n", |
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| 203 | "conv3_block3_1_conv (Conv2D) (None, 19, 19, 128) 24576 conv3_block3_0_relu[0][0] \n", |
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| 204 | "__________________________________________________________________________________________________\n", |
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| 205 | "conv3_block3_1_bn (BatchNormali (None, 19, 19, 128) 512 conv3_block3_1_conv[0][0] \n", |
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| 206 | "__________________________________________________________________________________________________\n", |
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| 207 | "conv3_block3_1_relu (Activation (None, 19, 19, 128) 0 conv3_block3_1_bn[0][0] \n", |
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| 208 | "__________________________________________________________________________________________________\n", |
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| 209 | "conv3_block3_2_conv (Conv2D) (None, 19, 19, 32) 36864 conv3_block3_1_relu[0][0] \n", |
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| 210 | "__________________________________________________________________________________________________\n", |
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| 211 | "conv3_block3_concat (Concatenat (None, 19, 19, 224) 0 conv3_block2_concat[0][0] \n", |
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| 212 | " conv3_block3_2_conv[0][0] \n", |
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| 213 | "__________________________________________________________________________________________________\n", |
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| 214 | "conv3_block4_0_bn (BatchNormali (None, 19, 19, 224) 896 conv3_block3_concat[0][0] \n", |
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| 215 | "__________________________________________________________________________________________________\n", |
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| 216 | "conv3_block4_0_relu (Activation (None, 19, 19, 224) 0 conv3_block4_0_bn[0][0] \n", |
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| 217 | "__________________________________________________________________________________________________\n", |
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| 218 | "conv3_block4_1_conv (Conv2D) (None, 19, 19, 128) 28672 conv3_block4_0_relu[0][0] \n", |
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| 219 | "__________________________________________________________________________________________________\n", |
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| 220 | "conv3_block4_1_bn (BatchNormali (None, 19, 19, 128) 512 conv3_block4_1_conv[0][0] \n", |
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| 221 | "__________________________________________________________________________________________________\n", |
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| 222 | "conv3_block4_1_relu (Activation (None, 19, 19, 128) 0 conv3_block4_1_bn[0][0] \n", |
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| 223 | "__________________________________________________________________________________________________\n", |
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| 224 | "conv3_block4_2_conv (Conv2D) (None, 19, 19, 32) 36864 conv3_block4_1_relu[0][0] \n", |
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| 225 | "__________________________________________________________________________________________________\n", |
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| 226 | "conv3_block4_concat (Concatenat (None, 19, 19, 256) 0 conv3_block3_concat[0][0] \n", |
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| 227 | " conv3_block4_2_conv[0][0] \n", |
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| 228 | "__________________________________________________________________________________________________\n", |
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| 229 | "conv3_block5_0_bn (BatchNormali (None, 19, 19, 256) 1024 conv3_block4_concat[0][0] \n", |
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| 230 | "__________________________________________________________________________________________________\n", |
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| 231 | "conv3_block5_0_relu (Activation (None, 19, 19, 256) 0 conv3_block5_0_bn[0][0] \n", |
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| 232 | "__________________________________________________________________________________________________\n", |
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| 233 | "conv3_block5_1_conv (Conv2D) (None, 19, 19, 128) 32768 conv3_block5_0_relu[0][0] \n", |
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| 234 | "__________________________________________________________________________________________________\n", |
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| 235 | "conv3_block5_1_bn (BatchNormali (None, 19, 19, 128) 512 conv3_block5_1_conv[0][0] \n", |
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| 236 | "__________________________________________________________________________________________________\n", |
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| 237 | "conv3_block5_1_relu (Activation (None, 19, 19, 128) 0 conv3_block5_1_bn[0][0] \n", |
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| 238 | "__________________________________________________________________________________________________\n", |
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| 239 | "conv3_block5_2_conv (Conv2D) (None, 19, 19, 32) 36864 conv3_block5_1_relu[0][0] \n", |
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| 240 | "__________________________________________________________________________________________________\n", |
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| 241 | "conv3_block5_concat (Concatenat (None, 19, 19, 288) 0 conv3_block4_concat[0][0] \n", |
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| 242 | " conv3_block5_2_conv[0][0] \n", |
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| 243 | "__________________________________________________________________________________________________\n", |
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| 244 | "conv3_block6_0_bn (BatchNormali (None, 19, 19, 288) 1152 conv3_block5_concat[0][0] \n", |
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| 245 | "__________________________________________________________________________________________________\n", |
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| 246 | "conv3_block6_0_relu (Activation (None, 19, 19, 288) 0 conv3_block6_0_bn[0][0] \n", |
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| 247 | "__________________________________________________________________________________________________\n", |
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| 248 | "conv3_block6_1_conv (Conv2D) (None, 19, 19, 128) 36864 conv3_block6_0_relu[0][0] \n", |
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| 249 | "__________________________________________________________________________________________________\n", |
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| 250 | "conv3_block6_1_bn (BatchNormali (None, 19, 19, 128) 512 conv3_block6_1_conv[0][0] \n", |
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| 251 | "__________________________________________________________________________________________________\n", |
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| 252 | "conv3_block6_1_relu (Activation (None, 19, 19, 128) 0 conv3_block6_1_bn[0][0] \n", |
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| 253 | "__________________________________________________________________________________________________\n", |
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| 254 | "conv3_block6_2_conv (Conv2D) (None, 19, 19, 32) 36864 conv3_block6_1_relu[0][0] \n", |
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| 255 | "__________________________________________________________________________________________________\n", |
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| 256 | "conv3_block6_concat (Concatenat (None, 19, 19, 320) 0 conv3_block5_concat[0][0] \n", |
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| 257 | " conv3_block6_2_conv[0][0] \n", |
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| 258 | "__________________________________________________________________________________________________\n", |
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| 259 | "conv3_block7_0_bn (BatchNormali (None, 19, 19, 320) 1280 conv3_block6_concat[0][0] \n", |
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| 260 | "__________________________________________________________________________________________________\n", |
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| 261 | "conv3_block7_0_relu (Activation (None, 19, 19, 320) 0 conv3_block7_0_bn[0][0] \n", |
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| 262 | "__________________________________________________________________________________________________\n", |
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| 263 | "conv3_block7_1_conv (Conv2D) (None, 19, 19, 128) 40960 conv3_block7_0_relu[0][0] \n", |
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| 264 | "__________________________________________________________________________________________________\n", |
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| 265 | "conv3_block7_1_bn (BatchNormali (None, 19, 19, 128) 512 conv3_block7_1_conv[0][0] \n", |
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| 266 | "__________________________________________________________________________________________________\n", |
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| 267 | "conv3_block7_1_relu (Activation (None, 19, 19, 128) 0 conv3_block7_1_bn[0][0] \n", |
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| 268 | "__________________________________________________________________________________________________\n", |
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| 269 | "conv3_block7_2_conv (Conv2D) (None, 19, 19, 32) 36864 conv3_block7_1_relu[0][0] \n", |
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| 270 | "__________________________________________________________________________________________________\n", |
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| 271 | "conv3_block7_concat (Concatenat (None, 19, 19, 352) 0 conv3_block6_concat[0][0] \n", |
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| 272 | " conv3_block7_2_conv[0][0] \n", |
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| 273 | "__________________________________________________________________________________________________\n", |
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| 274 | "conv3_block8_0_bn (BatchNormali (None, 19, 19, 352) 1408 conv3_block7_concat[0][0] \n", |
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| 275 | "__________________________________________________________________________________________________\n", |
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| 276 | "conv3_block8_0_relu (Activation (None, 19, 19, 352) 0 conv3_block8_0_bn[0][0] \n", |
||
| 277 | "__________________________________________________________________________________________________\n", |
||
| 278 | "conv3_block8_1_conv (Conv2D) (None, 19, 19, 128) 45056 conv3_block8_0_relu[0][0] \n", |
||
| 279 | "__________________________________________________________________________________________________\n", |
||
| 280 | "conv3_block8_1_bn (BatchNormali (None, 19, 19, 128) 512 conv3_block8_1_conv[0][0] \n", |
||
| 281 | "__________________________________________________________________________________________________\n", |
||
| 282 | "conv3_block8_1_relu (Activation (None, 19, 19, 128) 0 conv3_block8_1_bn[0][0] \n", |
||
| 283 | "__________________________________________________________________________________________________\n", |
||
| 284 | "conv3_block8_2_conv (Conv2D) (None, 19, 19, 32) 36864 conv3_block8_1_relu[0][0] \n", |
||
| 285 | "__________________________________________________________________________________________________\n", |
||
| 286 | "conv3_block8_concat (Concatenat (None, 19, 19, 384) 0 conv3_block7_concat[0][0] \n", |
||
| 287 | " conv3_block8_2_conv[0][0] \n", |
||
| 288 | "__________________________________________________________________________________________________\n", |
||
| 289 | "conv3_block9_0_bn (BatchNormali (None, 19, 19, 384) 1536 conv3_block8_concat[0][0] \n", |
||
| 290 | "__________________________________________________________________________________________________\n", |
||
| 291 | "conv3_block9_0_relu (Activation (None, 19, 19, 384) 0 conv3_block9_0_bn[0][0] \n", |
||
| 292 | "__________________________________________________________________________________________________\n", |
||
| 293 | "conv3_block9_1_conv (Conv2D) (None, 19, 19, 128) 49152 conv3_block9_0_relu[0][0] \n", |
||
| 294 | "__________________________________________________________________________________________________\n", |
||
| 295 | "conv3_block9_1_bn (BatchNormali (None, 19, 19, 128) 512 conv3_block9_1_conv[0][0] \n", |
||
| 296 | "__________________________________________________________________________________________________\n", |
||
| 297 | "conv3_block9_1_relu (Activation (None, 19, 19, 128) 0 conv3_block9_1_bn[0][0] \n", |
||
| 298 | "__________________________________________________________________________________________________\n", |
||
| 299 | "conv3_block9_2_conv (Conv2D) (None, 19, 19, 32) 36864 conv3_block9_1_relu[0][0] \n", |
||
| 300 | "__________________________________________________________________________________________________\n", |
||
| 301 | "conv3_block9_concat (Concatenat (None, 19, 19, 416) 0 conv3_block8_concat[0][0] \n", |
||
| 302 | " conv3_block9_2_conv[0][0] \n", |
||
| 303 | "__________________________________________________________________________________________________\n", |
||
| 304 | "conv3_block10_0_bn (BatchNormal (None, 19, 19, 416) 1664 conv3_block9_concat[0][0] \n", |
||
| 305 | "__________________________________________________________________________________________________\n", |
||
| 306 | "conv3_block10_0_relu (Activatio (None, 19, 19, 416) 0 conv3_block10_0_bn[0][0] \n", |
||
| 307 | "__________________________________________________________________________________________________\n", |
||
| 308 | "conv3_block10_1_conv (Conv2D) (None, 19, 19, 128) 53248 conv3_block10_0_relu[0][0] \n", |
||
| 309 | "__________________________________________________________________________________________________\n", |
||
| 310 | "conv3_block10_1_bn (BatchNormal (None, 19, 19, 128) 512 conv3_block10_1_conv[0][0] \n", |
||
| 311 | "__________________________________________________________________________________________________\n", |
||
| 312 | "conv3_block10_1_relu (Activatio (None, 19, 19, 128) 0 conv3_block10_1_bn[0][0] \n", |
||
| 313 | "__________________________________________________________________________________________________\n", |
||
| 314 | "conv3_block10_2_conv (Conv2D) (None, 19, 19, 32) 36864 conv3_block10_1_relu[0][0] \n", |
||
| 315 | "__________________________________________________________________________________________________\n", |
||
| 316 | "conv3_block10_concat (Concatena (None, 19, 19, 448) 0 conv3_block9_concat[0][0] \n", |
||
| 317 | " conv3_block10_2_conv[0][0] \n", |
||
| 318 | "__________________________________________________________________________________________________\n", |
||
| 319 | "conv3_block11_0_bn (BatchNormal (None, 19, 19, 448) 1792 conv3_block10_concat[0][0] \n", |
||
| 320 | "__________________________________________________________________________________________________\n", |
||
| 321 | "conv3_block11_0_relu (Activatio (None, 19, 19, 448) 0 conv3_block11_0_bn[0][0] \n", |
||
| 322 | "__________________________________________________________________________________________________\n", |
||
| 323 | "conv3_block11_1_conv (Conv2D) (None, 19, 19, 128) 57344 conv3_block11_0_relu[0][0] \n", |
||
| 324 | "__________________________________________________________________________________________________\n", |
||
| 325 | "conv3_block11_1_bn (BatchNormal (None, 19, 19, 128) 512 conv3_block11_1_conv[0][0] \n", |
||
| 326 | "__________________________________________________________________________________________________\n", |
||
| 327 | "conv3_block11_1_relu (Activatio (None, 19, 19, 128) 0 conv3_block11_1_bn[0][0] \n", |
||
| 328 | "__________________________________________________________________________________________________\n", |
||
| 329 | "conv3_block11_2_conv (Conv2D) (None, 19, 19, 32) 36864 conv3_block11_1_relu[0][0] \n", |
||
| 330 | "__________________________________________________________________________________________________\n", |
||
| 331 | "conv3_block11_concat (Concatena (None, 19, 19, 480) 0 conv3_block10_concat[0][0] \n", |
||
| 332 | " conv3_block11_2_conv[0][0] \n", |
||
| 333 | "__________________________________________________________________________________________________\n", |
||
| 334 | "conv3_block12_0_bn (BatchNormal (None, 19, 19, 480) 1920 conv3_block11_concat[0][0] \n", |
||
| 335 | "__________________________________________________________________________________________________\n", |
||
| 336 | "conv3_block12_0_relu (Activatio (None, 19, 19, 480) 0 conv3_block12_0_bn[0][0] \n", |
||
| 337 | "__________________________________________________________________________________________________\n", |
||
| 338 | "conv3_block12_1_conv (Conv2D) (None, 19, 19, 128) 61440 conv3_block12_0_relu[0][0] \n", |
||
| 339 | "__________________________________________________________________________________________________\n", |
||
| 340 | "conv3_block12_1_bn (BatchNormal (None, 19, 19, 128) 512 conv3_block12_1_conv[0][0] \n", |
||
| 341 | "__________________________________________________________________________________________________\n", |
||
| 342 | "conv3_block12_1_relu (Activatio (None, 19, 19, 128) 0 conv3_block12_1_bn[0][0] \n", |
||
| 343 | "__________________________________________________________________________________________________\n", |
||
| 344 | "conv3_block12_2_conv (Conv2D) (None, 19, 19, 32) 36864 conv3_block12_1_relu[0][0] \n", |
||
| 345 | "__________________________________________________________________________________________________\n", |
||
| 346 | "conv3_block12_concat (Concatena (None, 19, 19, 512) 0 conv3_block11_concat[0][0] \n", |
||
| 347 | " conv3_block12_2_conv[0][0] \n", |
||
| 348 | "__________________________________________________________________________________________________\n", |
||
| 349 | "pool3_bn (BatchNormalization) (None, 19, 19, 512) 2048 conv3_block12_concat[0][0] \n", |
||
| 350 | "__________________________________________________________________________________________________\n", |
||
| 351 | "pool3_relu (Activation) (None, 19, 19, 512) 0 pool3_bn[0][0] \n", |
||
| 352 | "__________________________________________________________________________________________________\n", |
||
| 353 | "pool3_conv (Conv2D) (None, 19, 19, 256) 131072 pool3_relu[0][0] \n", |
||
| 354 | "__________________________________________________________________________________________________\n", |
||
| 355 | "pool3_pool (AveragePooling2D) (None, 9, 9, 256) 0 pool3_conv[0][0] \n", |
||
| 356 | "__________________________________________________________________________________________________\n", |
||
| 357 | "conv4_block1_0_bn (BatchNormali (None, 9, 9, 256) 1024 pool3_pool[0][0] \n", |
||
| 358 | "__________________________________________________________________________________________________\n", |
||
| 359 | "conv4_block1_0_relu (Activation (None, 9, 9, 256) 0 conv4_block1_0_bn[0][0] \n", |
||
| 360 | "__________________________________________________________________________________________________\n", |
||
| 361 | "conv4_block1_1_conv (Conv2D) (None, 9, 9, 128) 32768 conv4_block1_0_relu[0][0] \n", |
||
| 362 | "__________________________________________________________________________________________________\n", |
||
| 363 | "conv4_block1_1_bn (BatchNormali (None, 9, 9, 128) 512 conv4_block1_1_conv[0][0] \n", |
||
| 364 | "__________________________________________________________________________________________________\n", |
||
| 365 | "conv4_block1_1_relu (Activation (None, 9, 9, 128) 0 conv4_block1_1_bn[0][0] \n", |
||
| 366 | "__________________________________________________________________________________________________\n", |
||
| 367 | "conv4_block1_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block1_1_relu[0][0] \n", |
||
| 368 | "__________________________________________________________________________________________________\n", |
||
| 369 | "conv4_block1_concat (Concatenat (None, 9, 9, 288) 0 pool3_pool[0][0] \n", |
||
| 370 | " conv4_block1_2_conv[0][0] \n", |
||
| 371 | "__________________________________________________________________________________________________\n", |
||
| 372 | "conv4_block2_0_bn (BatchNormali (None, 9, 9, 288) 1152 conv4_block1_concat[0][0] \n", |
||
| 373 | "__________________________________________________________________________________________________\n", |
||
| 374 | "conv4_block2_0_relu (Activation (None, 9, 9, 288) 0 conv4_block2_0_bn[0][0] \n", |
||
| 375 | "__________________________________________________________________________________________________\n", |
||
| 376 | "conv4_block2_1_conv (Conv2D) (None, 9, 9, 128) 36864 conv4_block2_0_relu[0][0] \n", |
||
| 377 | "__________________________________________________________________________________________________\n", |
||
| 378 | "conv4_block2_1_bn (BatchNormali (None, 9, 9, 128) 512 conv4_block2_1_conv[0][0] \n", |
||
| 379 | "__________________________________________________________________________________________________\n", |
||
| 380 | "conv4_block2_1_relu (Activation (None, 9, 9, 128) 0 conv4_block2_1_bn[0][0] \n", |
||
| 381 | "__________________________________________________________________________________________________\n", |
||
| 382 | "conv4_block2_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block2_1_relu[0][0] \n", |
||
| 383 | "__________________________________________________________________________________________________\n", |
||
| 384 | "conv4_block2_concat (Concatenat (None, 9, 9, 320) 0 conv4_block1_concat[0][0] \n", |
||
| 385 | " conv4_block2_2_conv[0][0] \n", |
||
| 386 | "__________________________________________________________________________________________________\n", |
||
| 387 | "conv4_block3_0_bn (BatchNormali (None, 9, 9, 320) 1280 conv4_block2_concat[0][0] \n", |
||
| 388 | "__________________________________________________________________________________________________\n", |
||
| 389 | "conv4_block3_0_relu (Activation (None, 9, 9, 320) 0 conv4_block3_0_bn[0][0] \n", |
||
| 390 | "__________________________________________________________________________________________________\n", |
||
| 391 | "conv4_block3_1_conv (Conv2D) (None, 9, 9, 128) 40960 conv4_block3_0_relu[0][0] \n", |
||
| 392 | "__________________________________________________________________________________________________\n", |
||
| 393 | "conv4_block3_1_bn (BatchNormali (None, 9, 9, 128) 512 conv4_block3_1_conv[0][0] \n", |
||
| 394 | "__________________________________________________________________________________________________\n", |
||
| 395 | "conv4_block3_1_relu (Activation (None, 9, 9, 128) 0 conv4_block3_1_bn[0][0] \n", |
||
| 396 | "__________________________________________________________________________________________________\n", |
||
| 397 | "conv4_block3_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block3_1_relu[0][0] \n", |
||
| 398 | "__________________________________________________________________________________________________\n", |
||
| 399 | "conv4_block3_concat (Concatenat (None, 9, 9, 352) 0 conv4_block2_concat[0][0] \n", |
||
| 400 | " conv4_block3_2_conv[0][0] \n", |
||
| 401 | "__________________________________________________________________________________________________\n", |
||
| 402 | "conv4_block4_0_bn (BatchNormali (None, 9, 9, 352) 1408 conv4_block3_concat[0][0] \n", |
||
| 403 | "__________________________________________________________________________________________________\n", |
||
| 404 | "conv4_block4_0_relu (Activation (None, 9, 9, 352) 0 conv4_block4_0_bn[0][0] \n", |
||
| 405 | "__________________________________________________________________________________________________\n", |
||
| 406 | "conv4_block4_1_conv (Conv2D) (None, 9, 9, 128) 45056 conv4_block4_0_relu[0][0] \n", |
||
| 407 | "__________________________________________________________________________________________________\n", |
||
| 408 | "conv4_block4_1_bn (BatchNormali (None, 9, 9, 128) 512 conv4_block4_1_conv[0][0] \n", |
||
| 409 | "__________________________________________________________________________________________________\n", |
||
| 410 | "conv4_block4_1_relu (Activation (None, 9, 9, 128) 0 conv4_block4_1_bn[0][0] \n", |
||
| 411 | "__________________________________________________________________________________________________\n", |
||
| 412 | "conv4_block4_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block4_1_relu[0][0] \n", |
||
| 413 | "__________________________________________________________________________________________________\n", |
||
| 414 | "conv4_block4_concat (Concatenat (None, 9, 9, 384) 0 conv4_block3_concat[0][0] \n", |
||
| 415 | " conv4_block4_2_conv[0][0] \n", |
||
| 416 | "__________________________________________________________________________________________________\n", |
||
| 417 | "conv4_block5_0_bn (BatchNormali (None, 9, 9, 384) 1536 conv4_block4_concat[0][0] \n", |
||
| 418 | "__________________________________________________________________________________________________\n", |
||
| 419 | "conv4_block5_0_relu (Activation (None, 9, 9, 384) 0 conv4_block5_0_bn[0][0] \n", |
||
| 420 | "__________________________________________________________________________________________________\n", |
||
| 421 | "conv4_block5_1_conv (Conv2D) (None, 9, 9, 128) 49152 conv4_block5_0_relu[0][0] \n", |
||
| 422 | "__________________________________________________________________________________________________\n", |
||
| 423 | "conv4_block5_1_bn (BatchNormali (None, 9, 9, 128) 512 conv4_block5_1_conv[0][0] \n", |
||
| 424 | "__________________________________________________________________________________________________\n", |
||
| 425 | "conv4_block5_1_relu (Activation (None, 9, 9, 128) 0 conv4_block5_1_bn[0][0] \n", |
||
| 426 | "__________________________________________________________________________________________________\n", |
||
| 427 | "conv4_block5_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block5_1_relu[0][0] \n", |
||
| 428 | "__________________________________________________________________________________________________\n", |
||
| 429 | "conv4_block5_concat (Concatenat (None, 9, 9, 416) 0 conv4_block4_concat[0][0] \n", |
||
| 430 | " conv4_block5_2_conv[0][0] \n", |
||
| 431 | "__________________________________________________________________________________________________\n", |
||
| 432 | "conv4_block6_0_bn (BatchNormali (None, 9, 9, 416) 1664 conv4_block5_concat[0][0] \n", |
||
| 433 | "__________________________________________________________________________________________________\n", |
||
| 434 | "conv4_block6_0_relu (Activation (None, 9, 9, 416) 0 conv4_block6_0_bn[0][0] \n", |
||
| 435 | "__________________________________________________________________________________________________\n", |
||
| 436 | "conv4_block6_1_conv (Conv2D) (None, 9, 9, 128) 53248 conv4_block6_0_relu[0][0] \n", |
||
| 437 | "__________________________________________________________________________________________________\n", |
||
| 438 | "conv4_block6_1_bn (BatchNormali (None, 9, 9, 128) 512 conv4_block6_1_conv[0][0] \n", |
||
| 439 | "__________________________________________________________________________________________________\n", |
||
| 440 | "conv4_block6_1_relu (Activation (None, 9, 9, 128) 0 conv4_block6_1_bn[0][0] \n", |
||
| 441 | "__________________________________________________________________________________________________\n", |
||
| 442 | "conv4_block6_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block6_1_relu[0][0] \n", |
||
| 443 | "__________________________________________________________________________________________________\n", |
||
| 444 | "conv4_block6_concat (Concatenat (None, 9, 9, 448) 0 conv4_block5_concat[0][0] \n", |
||
| 445 | " conv4_block6_2_conv[0][0] \n", |
||
| 446 | "__________________________________________________________________________________________________\n", |
||
| 447 | "conv4_block7_0_bn (BatchNormali (None, 9, 9, 448) 1792 conv4_block6_concat[0][0] \n", |
||
| 448 | "__________________________________________________________________________________________________\n", |
||
| 449 | "conv4_block7_0_relu (Activation (None, 9, 9, 448) 0 conv4_block7_0_bn[0][0] \n", |
||
| 450 | "__________________________________________________________________________________________________\n", |
||
| 451 | "conv4_block7_1_conv (Conv2D) (None, 9, 9, 128) 57344 conv4_block7_0_relu[0][0] \n", |
||
| 452 | "__________________________________________________________________________________________________\n", |
||
| 453 | "conv4_block7_1_bn (BatchNormali (None, 9, 9, 128) 512 conv4_block7_1_conv[0][0] \n", |
||
| 454 | "__________________________________________________________________________________________________\n", |
||
| 455 | "conv4_block7_1_relu (Activation (None, 9, 9, 128) 0 conv4_block7_1_bn[0][0] \n", |
||
| 456 | "__________________________________________________________________________________________________\n", |
||
| 457 | "conv4_block7_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block7_1_relu[0][0] \n", |
||
| 458 | "__________________________________________________________________________________________________\n", |
||
| 459 | "conv4_block7_concat (Concatenat (None, 9, 9, 480) 0 conv4_block6_concat[0][0] \n", |
||
| 460 | " conv4_block7_2_conv[0][0] \n", |
||
| 461 | "__________________________________________________________________________________________________\n", |
||
| 462 | "conv4_block8_0_bn (BatchNormali (None, 9, 9, 480) 1920 conv4_block7_concat[0][0] \n", |
||
| 463 | "__________________________________________________________________________________________________\n", |
||
| 464 | "conv4_block8_0_relu (Activation (None, 9, 9, 480) 0 conv4_block8_0_bn[0][0] \n", |
||
| 465 | "__________________________________________________________________________________________________\n", |
||
| 466 | "conv4_block8_1_conv (Conv2D) (None, 9, 9, 128) 61440 conv4_block8_0_relu[0][0] \n", |
||
| 467 | "__________________________________________________________________________________________________\n", |
||
| 468 | "conv4_block8_1_bn (BatchNormali (None, 9, 9, 128) 512 conv4_block8_1_conv[0][0] \n", |
||
| 469 | "__________________________________________________________________________________________________\n", |
||
| 470 | "conv4_block8_1_relu (Activation (None, 9, 9, 128) 0 conv4_block8_1_bn[0][0] \n", |
||
| 471 | "__________________________________________________________________________________________________\n", |
||
| 472 | "conv4_block8_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block8_1_relu[0][0] \n", |
||
| 473 | "__________________________________________________________________________________________________\n", |
||
| 474 | "conv4_block8_concat (Concatenat (None, 9, 9, 512) 0 conv4_block7_concat[0][0] \n", |
||
| 475 | " conv4_block8_2_conv[0][0] \n", |
||
| 476 | "__________________________________________________________________________________________________\n", |
||
| 477 | "conv4_block9_0_bn (BatchNormali (None, 9, 9, 512) 2048 conv4_block8_concat[0][0] \n", |
||
| 478 | "__________________________________________________________________________________________________\n", |
||
| 479 | "conv4_block9_0_relu (Activation (None, 9, 9, 512) 0 conv4_block9_0_bn[0][0] \n", |
||
| 480 | "__________________________________________________________________________________________________\n", |
||
| 481 | "conv4_block9_1_conv (Conv2D) (None, 9, 9, 128) 65536 conv4_block9_0_relu[0][0] \n", |
||
| 482 | "__________________________________________________________________________________________________\n", |
||
| 483 | "conv4_block9_1_bn (BatchNormali (None, 9, 9, 128) 512 conv4_block9_1_conv[0][0] \n", |
||
| 484 | "__________________________________________________________________________________________________\n", |
||
| 485 | "conv4_block9_1_relu (Activation (None, 9, 9, 128) 0 conv4_block9_1_bn[0][0] \n", |
||
| 486 | "__________________________________________________________________________________________________\n", |
||
| 487 | "conv4_block9_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block9_1_relu[0][0] \n", |
||
| 488 | "__________________________________________________________________________________________________\n", |
||
| 489 | "conv4_block9_concat (Concatenat (None, 9, 9, 544) 0 conv4_block8_concat[0][0] \n", |
||
| 490 | " conv4_block9_2_conv[0][0] \n", |
||
| 491 | "__________________________________________________________________________________________________\n", |
||
| 492 | "conv4_block10_0_bn (BatchNormal (None, 9, 9, 544) 2176 conv4_block9_concat[0][0] \n", |
||
| 493 | "__________________________________________________________________________________________________\n", |
||
| 494 | "conv4_block10_0_relu (Activatio (None, 9, 9, 544) 0 conv4_block10_0_bn[0][0] \n", |
||
| 495 | "__________________________________________________________________________________________________\n", |
||
| 496 | "conv4_block10_1_conv (Conv2D) (None, 9, 9, 128) 69632 conv4_block10_0_relu[0][0] \n", |
||
| 497 | "__________________________________________________________________________________________________\n", |
||
| 498 | "conv4_block10_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block10_1_conv[0][0] \n", |
||
| 499 | "__________________________________________________________________________________________________\n", |
||
| 500 | "conv4_block10_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block10_1_bn[0][0] \n", |
||
| 501 | "__________________________________________________________________________________________________\n", |
||
| 502 | "conv4_block10_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block10_1_relu[0][0] \n", |
||
| 503 | "__________________________________________________________________________________________________\n", |
||
| 504 | "conv4_block10_concat (Concatena (None, 9, 9, 576) 0 conv4_block9_concat[0][0] \n", |
||
| 505 | " conv4_block10_2_conv[0][0] \n", |
||
| 506 | "__________________________________________________________________________________________________\n", |
||
| 507 | "conv4_block11_0_bn (BatchNormal (None, 9, 9, 576) 2304 conv4_block10_concat[0][0] \n", |
||
| 508 | "__________________________________________________________________________________________________\n", |
||
| 509 | "conv4_block11_0_relu (Activatio (None, 9, 9, 576) 0 conv4_block11_0_bn[0][0] \n", |
||
| 510 | "__________________________________________________________________________________________________\n", |
||
| 511 | "conv4_block11_1_conv (Conv2D) (None, 9, 9, 128) 73728 conv4_block11_0_relu[0][0] \n", |
||
| 512 | "__________________________________________________________________________________________________\n", |
||
| 513 | "conv4_block11_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block11_1_conv[0][0] \n", |
||
| 514 | "__________________________________________________________________________________________________\n", |
||
| 515 | "conv4_block11_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block11_1_bn[0][0] \n", |
||
| 516 | "__________________________________________________________________________________________________\n", |
||
| 517 | "conv4_block11_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block11_1_relu[0][0] \n", |
||
| 518 | "__________________________________________________________________________________________________\n", |
||
| 519 | "conv4_block11_concat (Concatena (None, 9, 9, 608) 0 conv4_block10_concat[0][0] \n", |
||
| 520 | " conv4_block11_2_conv[0][0] \n", |
||
| 521 | "__________________________________________________________________________________________________\n", |
||
| 522 | "conv4_block12_0_bn (BatchNormal (None, 9, 9, 608) 2432 conv4_block11_concat[0][0] \n", |
||
| 523 | "__________________________________________________________________________________________________\n", |
||
| 524 | "conv4_block12_0_relu (Activatio (None, 9, 9, 608) 0 conv4_block12_0_bn[0][0] \n", |
||
| 525 | "__________________________________________________________________________________________________\n", |
||
| 526 | "conv4_block12_1_conv (Conv2D) (None, 9, 9, 128) 77824 conv4_block12_0_relu[0][0] \n", |
||
| 527 | "__________________________________________________________________________________________________\n", |
||
| 528 | "conv4_block12_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block12_1_conv[0][0] \n", |
||
| 529 | "__________________________________________________________________________________________________\n", |
||
| 530 | "conv4_block12_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block12_1_bn[0][0] \n", |
||
| 531 | "__________________________________________________________________________________________________\n", |
||
| 532 | "conv4_block12_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block12_1_relu[0][0] \n", |
||
| 533 | "__________________________________________________________________________________________________\n", |
||
| 534 | "conv4_block12_concat (Concatena (None, 9, 9, 640) 0 conv4_block11_concat[0][0] \n", |
||
| 535 | " conv4_block12_2_conv[0][0] \n", |
||
| 536 | "__________________________________________________________________________________________________\n", |
||
| 537 | "conv4_block13_0_bn (BatchNormal (None, 9, 9, 640) 2560 conv4_block12_concat[0][0] \n", |
||
| 538 | "__________________________________________________________________________________________________\n", |
||
| 539 | "conv4_block13_0_relu (Activatio (None, 9, 9, 640) 0 conv4_block13_0_bn[0][0] \n", |
||
| 540 | "__________________________________________________________________________________________________\n", |
||
| 541 | "conv4_block13_1_conv (Conv2D) (None, 9, 9, 128) 81920 conv4_block13_0_relu[0][0] \n", |
||
| 542 | "__________________________________________________________________________________________________\n", |
||
| 543 | "conv4_block13_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block13_1_conv[0][0] \n", |
||
| 544 | "__________________________________________________________________________________________________\n", |
||
| 545 | "conv4_block13_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block13_1_bn[0][0] \n", |
||
| 546 | "__________________________________________________________________________________________________\n", |
||
| 547 | "conv4_block13_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block13_1_relu[0][0] \n", |
||
| 548 | "__________________________________________________________________________________________________\n", |
||
| 549 | "conv4_block13_concat (Concatena (None, 9, 9, 672) 0 conv4_block12_concat[0][0] \n", |
||
| 550 | " conv4_block13_2_conv[0][0] \n", |
||
| 551 | "__________________________________________________________________________________________________\n", |
||
| 552 | "conv4_block14_0_bn (BatchNormal (None, 9, 9, 672) 2688 conv4_block13_concat[0][0] \n", |
||
| 553 | "__________________________________________________________________________________________________\n", |
||
| 554 | "conv4_block14_0_relu (Activatio (None, 9, 9, 672) 0 conv4_block14_0_bn[0][0] \n", |
||
| 555 | "__________________________________________________________________________________________________\n", |
||
| 556 | "conv4_block14_1_conv (Conv2D) (None, 9, 9, 128) 86016 conv4_block14_0_relu[0][0] \n", |
||
| 557 | "__________________________________________________________________________________________________\n", |
||
| 558 | "conv4_block14_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block14_1_conv[0][0] \n", |
||
| 559 | "__________________________________________________________________________________________________\n", |
||
| 560 | "conv4_block14_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block14_1_bn[0][0] \n", |
||
| 561 | "__________________________________________________________________________________________________\n", |
||
| 562 | "conv4_block14_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block14_1_relu[0][0] \n", |
||
| 563 | "__________________________________________________________________________________________________\n", |
||
| 564 | "conv4_block14_concat (Concatena (None, 9, 9, 704) 0 conv4_block13_concat[0][0] \n", |
||
| 565 | " conv4_block14_2_conv[0][0] \n", |
||
| 566 | "__________________________________________________________________________________________________\n", |
||
| 567 | "conv4_block15_0_bn (BatchNormal (None, 9, 9, 704) 2816 conv4_block14_concat[0][0] \n", |
||
| 568 | "__________________________________________________________________________________________________\n", |
||
| 569 | "conv4_block15_0_relu (Activatio (None, 9, 9, 704) 0 conv4_block15_0_bn[0][0] \n", |
||
| 570 | "__________________________________________________________________________________________________\n", |
||
| 571 | "conv4_block15_1_conv (Conv2D) (None, 9, 9, 128) 90112 conv4_block15_0_relu[0][0] \n", |
||
| 572 | "__________________________________________________________________________________________________\n", |
||
| 573 | "conv4_block15_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block15_1_conv[0][0] \n", |
||
| 574 | "__________________________________________________________________________________________________\n", |
||
| 575 | "conv4_block15_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block15_1_bn[0][0] \n", |
||
| 576 | "__________________________________________________________________________________________________\n", |
||
| 577 | "conv4_block15_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block15_1_relu[0][0] \n", |
||
| 578 | "__________________________________________________________________________________________________\n", |
||
| 579 | "conv4_block15_concat (Concatena (None, 9, 9, 736) 0 conv4_block14_concat[0][0] \n", |
||
| 580 | " conv4_block15_2_conv[0][0] \n", |
||
| 581 | "__________________________________________________________________________________________________\n", |
||
| 582 | "conv4_block16_0_bn (BatchNormal (None, 9, 9, 736) 2944 conv4_block15_concat[0][0] \n", |
||
| 583 | "__________________________________________________________________________________________________\n", |
||
| 584 | "conv4_block16_0_relu (Activatio (None, 9, 9, 736) 0 conv4_block16_0_bn[0][0] \n", |
||
| 585 | "__________________________________________________________________________________________________\n", |
||
| 586 | "conv4_block16_1_conv (Conv2D) (None, 9, 9, 128) 94208 conv4_block16_0_relu[0][0] \n", |
||
| 587 | "__________________________________________________________________________________________________\n", |
||
| 588 | "conv4_block16_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block16_1_conv[0][0] \n", |
||
| 589 | "__________________________________________________________________________________________________\n", |
||
| 590 | "conv4_block16_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block16_1_bn[0][0] \n", |
||
| 591 | "__________________________________________________________________________________________________\n", |
||
| 592 | "conv4_block16_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block16_1_relu[0][0] \n", |
||
| 593 | "__________________________________________________________________________________________________\n", |
||
| 594 | "conv4_block16_concat (Concatena (None, 9, 9, 768) 0 conv4_block15_concat[0][0] \n", |
||
| 595 | " conv4_block16_2_conv[0][0] \n", |
||
| 596 | "__________________________________________________________________________________________________\n", |
||
| 597 | "conv4_block17_0_bn (BatchNormal (None, 9, 9, 768) 3072 conv4_block16_concat[0][0] \n", |
||
| 598 | "__________________________________________________________________________________________________\n", |
||
| 599 | "conv4_block17_0_relu (Activatio (None, 9, 9, 768) 0 conv4_block17_0_bn[0][0] \n", |
||
| 600 | "__________________________________________________________________________________________________\n", |
||
| 601 | "conv4_block17_1_conv (Conv2D) (None, 9, 9, 128) 98304 conv4_block17_0_relu[0][0] \n", |
||
| 602 | "__________________________________________________________________________________________________\n", |
||
| 603 | "conv4_block17_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block17_1_conv[0][0] \n", |
||
| 604 | "__________________________________________________________________________________________________\n", |
||
| 605 | "conv4_block17_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block17_1_bn[0][0] \n", |
||
| 606 | "__________________________________________________________________________________________________\n", |
||
| 607 | "conv4_block17_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block17_1_relu[0][0] \n", |
||
| 608 | "__________________________________________________________________________________________________\n", |
||
| 609 | "conv4_block17_concat (Concatena (None, 9, 9, 800) 0 conv4_block16_concat[0][0] \n", |
||
| 610 | " conv4_block17_2_conv[0][0] \n", |
||
| 611 | "__________________________________________________________________________________________________\n", |
||
| 612 | "conv4_block18_0_bn (BatchNormal (None, 9, 9, 800) 3200 conv4_block17_concat[0][0] \n", |
||
| 613 | "__________________________________________________________________________________________________\n", |
||
| 614 | "conv4_block18_0_relu (Activatio (None, 9, 9, 800) 0 conv4_block18_0_bn[0][0] \n", |
||
| 615 | "__________________________________________________________________________________________________\n", |
||
| 616 | "conv4_block18_1_conv (Conv2D) (None, 9, 9, 128) 102400 conv4_block18_0_relu[0][0] \n", |
||
| 617 | "__________________________________________________________________________________________________\n", |
||
| 618 | "conv4_block18_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block18_1_conv[0][0] \n", |
||
| 619 | "__________________________________________________________________________________________________\n", |
||
| 620 | "conv4_block18_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block18_1_bn[0][0] \n", |
||
| 621 | "__________________________________________________________________________________________________\n", |
||
| 622 | "conv4_block18_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block18_1_relu[0][0] \n", |
||
| 623 | "__________________________________________________________________________________________________\n", |
||
| 624 | "conv4_block18_concat (Concatena (None, 9, 9, 832) 0 conv4_block17_concat[0][0] \n", |
||
| 625 | " conv4_block18_2_conv[0][0] \n", |
||
| 626 | "__________________________________________________________________________________________________\n", |
||
| 627 | "conv4_block19_0_bn (BatchNormal (None, 9, 9, 832) 3328 conv4_block18_concat[0][0] \n", |
||
| 628 | "__________________________________________________________________________________________________\n", |
||
| 629 | "conv4_block19_0_relu (Activatio (None, 9, 9, 832) 0 conv4_block19_0_bn[0][0] \n", |
||
| 630 | "__________________________________________________________________________________________________\n", |
||
| 631 | "conv4_block19_1_conv (Conv2D) (None, 9, 9, 128) 106496 conv4_block19_0_relu[0][0] \n", |
||
| 632 | "__________________________________________________________________________________________________\n", |
||
| 633 | "conv4_block19_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block19_1_conv[0][0] \n", |
||
| 634 | "__________________________________________________________________________________________________\n", |
||
| 635 | "conv4_block19_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block19_1_bn[0][0] \n", |
||
| 636 | "__________________________________________________________________________________________________\n", |
||
| 637 | "conv4_block19_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block19_1_relu[0][0] \n", |
||
| 638 | "__________________________________________________________________________________________________\n", |
||
| 639 | "conv4_block19_concat (Concatena (None, 9, 9, 864) 0 conv4_block18_concat[0][0] \n", |
||
| 640 | " conv4_block19_2_conv[0][0] \n", |
||
| 641 | "__________________________________________________________________________________________________\n", |
||
| 642 | "conv4_block20_0_bn (BatchNormal (None, 9, 9, 864) 3456 conv4_block19_concat[0][0] \n", |
||
| 643 | "__________________________________________________________________________________________________\n", |
||
| 644 | "conv4_block20_0_relu (Activatio (None, 9, 9, 864) 0 conv4_block20_0_bn[0][0] \n", |
||
| 645 | "__________________________________________________________________________________________________\n", |
||
| 646 | "conv4_block20_1_conv (Conv2D) (None, 9, 9, 128) 110592 conv4_block20_0_relu[0][0] \n", |
||
| 647 | "__________________________________________________________________________________________________\n", |
||
| 648 | "conv4_block20_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block20_1_conv[0][0] \n", |
||
| 649 | "__________________________________________________________________________________________________\n", |
||
| 650 | "conv4_block20_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block20_1_bn[0][0] \n", |
||
| 651 | "__________________________________________________________________________________________________\n", |
||
| 652 | "conv4_block20_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block20_1_relu[0][0] \n", |
||
| 653 | "__________________________________________________________________________________________________\n", |
||
| 654 | "conv4_block20_concat (Concatena (None, 9, 9, 896) 0 conv4_block19_concat[0][0] \n", |
||
| 655 | " conv4_block20_2_conv[0][0] \n", |
||
| 656 | "__________________________________________________________________________________________________\n", |
||
| 657 | "conv4_block21_0_bn (BatchNormal (None, 9, 9, 896) 3584 conv4_block20_concat[0][0] \n", |
||
| 658 | "__________________________________________________________________________________________________\n", |
||
| 659 | "conv4_block21_0_relu (Activatio (None, 9, 9, 896) 0 conv4_block21_0_bn[0][0] \n", |
||
| 660 | "__________________________________________________________________________________________________\n", |
||
| 661 | "conv4_block21_1_conv (Conv2D) (None, 9, 9, 128) 114688 conv4_block21_0_relu[0][0] \n", |
||
| 662 | "__________________________________________________________________________________________________\n", |
||
| 663 | "conv4_block21_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block21_1_conv[0][0] \n", |
||
| 664 | "__________________________________________________________________________________________________\n", |
||
| 665 | "conv4_block21_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block21_1_bn[0][0] \n", |
||
| 666 | "__________________________________________________________________________________________________\n", |
||
| 667 | "conv4_block21_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block21_1_relu[0][0] \n", |
||
| 668 | "__________________________________________________________________________________________________\n", |
||
| 669 | "conv4_block21_concat (Concatena (None, 9, 9, 928) 0 conv4_block20_concat[0][0] \n", |
||
| 670 | " conv4_block21_2_conv[0][0] \n", |
||
| 671 | "__________________________________________________________________________________________________\n", |
||
| 672 | "conv4_block22_0_bn (BatchNormal (None, 9, 9, 928) 3712 conv4_block21_concat[0][0] \n", |
||
| 673 | "__________________________________________________________________________________________________\n", |
||
| 674 | "conv4_block22_0_relu (Activatio (None, 9, 9, 928) 0 conv4_block22_0_bn[0][0] \n", |
||
| 675 | "__________________________________________________________________________________________________\n", |
||
| 676 | "conv4_block22_1_conv (Conv2D) (None, 9, 9, 128) 118784 conv4_block22_0_relu[0][0] \n", |
||
| 677 | "__________________________________________________________________________________________________\n", |
||
| 678 | "conv4_block22_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block22_1_conv[0][0] \n", |
||
| 679 | "__________________________________________________________________________________________________\n", |
||
| 680 | "conv4_block22_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block22_1_bn[0][0] \n", |
||
| 681 | "__________________________________________________________________________________________________\n", |
||
| 682 | "conv4_block22_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block22_1_relu[0][0] \n", |
||
| 683 | "__________________________________________________________________________________________________\n", |
||
| 684 | "conv4_block22_concat (Concatena (None, 9, 9, 960) 0 conv4_block21_concat[0][0] \n", |
||
| 685 | " conv4_block22_2_conv[0][0] \n", |
||
| 686 | "__________________________________________________________________________________________________\n", |
||
| 687 | "conv4_block23_0_bn (BatchNormal (None, 9, 9, 960) 3840 conv4_block22_concat[0][0] \n", |
||
| 688 | "__________________________________________________________________________________________________\n", |
||
| 689 | "conv4_block23_0_relu (Activatio (None, 9, 9, 960) 0 conv4_block23_0_bn[0][0] \n", |
||
| 690 | "__________________________________________________________________________________________________\n", |
||
| 691 | "conv4_block23_1_conv (Conv2D) (None, 9, 9, 128) 122880 conv4_block23_0_relu[0][0] \n", |
||
| 692 | "__________________________________________________________________________________________________\n", |
||
| 693 | "conv4_block23_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block23_1_conv[0][0] \n", |
||
| 694 | "__________________________________________________________________________________________________\n", |
||
| 695 | "conv4_block23_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block23_1_bn[0][0] \n", |
||
| 696 | "__________________________________________________________________________________________________\n", |
||
| 697 | "conv4_block23_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block23_1_relu[0][0] \n", |
||
| 698 | "__________________________________________________________________________________________________\n", |
||
| 699 | "conv4_block23_concat (Concatena (None, 9, 9, 992) 0 conv4_block22_concat[0][0] \n", |
||
| 700 | " conv4_block23_2_conv[0][0] \n", |
||
| 701 | "__________________________________________________________________________________________________\n", |
||
| 702 | "conv4_block24_0_bn (BatchNormal (None, 9, 9, 992) 3968 conv4_block23_concat[0][0] \n", |
||
| 703 | "__________________________________________________________________________________________________\n", |
||
| 704 | "conv4_block24_0_relu (Activatio (None, 9, 9, 992) 0 conv4_block24_0_bn[0][0] \n", |
||
| 705 | "__________________________________________________________________________________________________\n", |
||
| 706 | "conv4_block24_1_conv (Conv2D) (None, 9, 9, 128) 126976 conv4_block24_0_relu[0][0] \n", |
||
| 707 | "__________________________________________________________________________________________________\n", |
||
| 708 | "conv4_block24_1_bn (BatchNormal (None, 9, 9, 128) 512 conv4_block24_1_conv[0][0] \n", |
||
| 709 | "__________________________________________________________________________________________________\n", |
||
| 710 | "conv4_block24_1_relu (Activatio (None, 9, 9, 128) 0 conv4_block24_1_bn[0][0] \n", |
||
| 711 | "__________________________________________________________________________________________________\n", |
||
| 712 | "conv4_block24_2_conv (Conv2D) (None, 9, 9, 32) 36864 conv4_block24_1_relu[0][0] \n", |
||
| 713 | "__________________________________________________________________________________________________\n", |
||
| 714 | "conv4_block24_concat (Concatena (None, 9, 9, 1024) 0 conv4_block23_concat[0][0] \n", |
||
| 715 | " conv4_block24_2_conv[0][0] \n", |
||
| 716 | "__________________________________________________________________________________________________\n", |
||
| 717 | "pool4_bn (BatchNormalization) (None, 9, 9, 1024) 4096 conv4_block24_concat[0][0] \n", |
||
| 718 | "__________________________________________________________________________________________________\n", |
||
| 719 | "pool4_relu (Activation) (None, 9, 9, 1024) 0 pool4_bn[0][0] \n", |
||
| 720 | "__________________________________________________________________________________________________\n", |
||
| 721 | "pool4_conv (Conv2D) (None, 9, 9, 512) 524288 pool4_relu[0][0] \n", |
||
| 722 | "__________________________________________________________________________________________________\n", |
||
| 723 | "pool4_pool (AveragePooling2D) (None, 4, 4, 512) 0 pool4_conv[0][0] \n", |
||
| 724 | "__________________________________________________________________________________________________\n", |
||
| 725 | "conv5_block1_0_bn (BatchNormali (None, 4, 4, 512) 2048 pool4_pool[0][0] \n", |
||
| 726 | "__________________________________________________________________________________________________\n", |
||
| 727 | "conv5_block1_0_relu (Activation (None, 4, 4, 512) 0 conv5_block1_0_bn[0][0] \n", |
||
| 728 | "__________________________________________________________________________________________________\n", |
||
| 729 | "conv5_block1_1_conv (Conv2D) (None, 4, 4, 128) 65536 conv5_block1_0_relu[0][0] \n", |
||
| 730 | "__________________________________________________________________________________________________\n", |
||
| 731 | "conv5_block1_1_bn (BatchNormali (None, 4, 4, 128) 512 conv5_block1_1_conv[0][0] \n", |
||
| 732 | "__________________________________________________________________________________________________\n", |
||
| 733 | "conv5_block1_1_relu (Activation (None, 4, 4, 128) 0 conv5_block1_1_bn[0][0] \n", |
||
| 734 | "__________________________________________________________________________________________________\n", |
||
| 735 | "conv5_block1_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block1_1_relu[0][0] \n", |
||
| 736 | "__________________________________________________________________________________________________\n", |
||
| 737 | "conv5_block1_concat (Concatenat (None, 4, 4, 544) 0 pool4_pool[0][0] \n", |
||
| 738 | " conv5_block1_2_conv[0][0] \n", |
||
| 739 | "__________________________________________________________________________________________________\n", |
||
| 740 | "conv5_block2_0_bn (BatchNormali (None, 4, 4, 544) 2176 conv5_block1_concat[0][0] \n", |
||
| 741 | "__________________________________________________________________________________________________\n", |
||
| 742 | "conv5_block2_0_relu (Activation (None, 4, 4, 544) 0 conv5_block2_0_bn[0][0] \n", |
||
| 743 | "__________________________________________________________________________________________________\n", |
||
| 744 | "conv5_block2_1_conv (Conv2D) (None, 4, 4, 128) 69632 conv5_block2_0_relu[0][0] \n", |
||
| 745 | "__________________________________________________________________________________________________\n", |
||
| 746 | "conv5_block2_1_bn (BatchNormali (None, 4, 4, 128) 512 conv5_block2_1_conv[0][0] \n", |
||
| 747 | "__________________________________________________________________________________________________\n", |
||
| 748 | "conv5_block2_1_relu (Activation (None, 4, 4, 128) 0 conv5_block2_1_bn[0][0] \n", |
||
| 749 | "__________________________________________________________________________________________________\n", |
||
| 750 | "conv5_block2_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block2_1_relu[0][0] \n", |
||
| 751 | "__________________________________________________________________________________________________\n", |
||
| 752 | "conv5_block2_concat (Concatenat (None, 4, 4, 576) 0 conv5_block1_concat[0][0] \n", |
||
| 753 | " conv5_block2_2_conv[0][0] \n", |
||
| 754 | "__________________________________________________________________________________________________\n", |
||
| 755 | "conv5_block3_0_bn (BatchNormali (None, 4, 4, 576) 2304 conv5_block2_concat[0][0] \n", |
||
| 756 | "__________________________________________________________________________________________________\n", |
||
| 757 | "conv5_block3_0_relu (Activation (None, 4, 4, 576) 0 conv5_block3_0_bn[0][0] \n", |
||
| 758 | "__________________________________________________________________________________________________\n", |
||
| 759 | "conv5_block3_1_conv (Conv2D) (None, 4, 4, 128) 73728 conv5_block3_0_relu[0][0] \n", |
||
| 760 | "__________________________________________________________________________________________________\n", |
||
| 761 | "conv5_block3_1_bn (BatchNormali (None, 4, 4, 128) 512 conv5_block3_1_conv[0][0] \n", |
||
| 762 | "__________________________________________________________________________________________________\n", |
||
| 763 | "conv5_block3_1_relu (Activation (None, 4, 4, 128) 0 conv5_block3_1_bn[0][0] \n", |
||
| 764 | "__________________________________________________________________________________________________\n", |
||
| 765 | "conv5_block3_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block3_1_relu[0][0] \n", |
||
| 766 | "__________________________________________________________________________________________________\n", |
||
| 767 | "conv5_block3_concat (Concatenat (None, 4, 4, 608) 0 conv5_block2_concat[0][0] \n", |
||
| 768 | " conv5_block3_2_conv[0][0] \n", |
||
| 769 | "__________________________________________________________________________________________________\n", |
||
| 770 | "conv5_block4_0_bn (BatchNormali (None, 4, 4, 608) 2432 conv5_block3_concat[0][0] \n", |
||
| 771 | "__________________________________________________________________________________________________\n", |
||
| 772 | "conv5_block4_0_relu (Activation (None, 4, 4, 608) 0 conv5_block4_0_bn[0][0] \n", |
||
| 773 | "__________________________________________________________________________________________________\n", |
||
| 774 | "conv5_block4_1_conv (Conv2D) (None, 4, 4, 128) 77824 conv5_block4_0_relu[0][0] \n", |
||
| 775 | "__________________________________________________________________________________________________\n", |
||
| 776 | "conv5_block4_1_bn (BatchNormali (None, 4, 4, 128) 512 conv5_block4_1_conv[0][0] \n", |
||
| 777 | "__________________________________________________________________________________________________\n", |
||
| 778 | "conv5_block4_1_relu (Activation (None, 4, 4, 128) 0 conv5_block4_1_bn[0][0] \n", |
||
| 779 | "__________________________________________________________________________________________________\n", |
||
| 780 | "conv5_block4_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block4_1_relu[0][0] \n", |
||
| 781 | "__________________________________________________________________________________________________\n", |
||
| 782 | "conv5_block4_concat (Concatenat (None, 4, 4, 640) 0 conv5_block3_concat[0][0] \n", |
||
| 783 | " conv5_block4_2_conv[0][0] \n", |
||
| 784 | "__________________________________________________________________________________________________\n", |
||
| 785 | "conv5_block5_0_bn (BatchNormali (None, 4, 4, 640) 2560 conv5_block4_concat[0][0] \n", |
||
| 786 | "__________________________________________________________________________________________________\n", |
||
| 787 | "conv5_block5_0_relu (Activation (None, 4, 4, 640) 0 conv5_block5_0_bn[0][0] \n", |
||
| 788 | "__________________________________________________________________________________________________\n", |
||
| 789 | "conv5_block5_1_conv (Conv2D) (None, 4, 4, 128) 81920 conv5_block5_0_relu[0][0] \n", |
||
| 790 | "__________________________________________________________________________________________________\n", |
||
| 791 | "conv5_block5_1_bn (BatchNormali (None, 4, 4, 128) 512 conv5_block5_1_conv[0][0] \n", |
||
| 792 | "__________________________________________________________________________________________________\n", |
||
| 793 | "conv5_block5_1_relu (Activation (None, 4, 4, 128) 0 conv5_block5_1_bn[0][0] \n", |
||
| 794 | "__________________________________________________________________________________________________\n", |
||
| 795 | "conv5_block5_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block5_1_relu[0][0] \n", |
||
| 796 | "__________________________________________________________________________________________________\n", |
||
| 797 | "conv5_block5_concat (Concatenat (None, 4, 4, 672) 0 conv5_block4_concat[0][0] \n", |
||
| 798 | " conv5_block5_2_conv[0][0] \n", |
||
| 799 | "__________________________________________________________________________________________________\n", |
||
| 800 | "conv5_block6_0_bn (BatchNormali (None, 4, 4, 672) 2688 conv5_block5_concat[0][0] \n", |
||
| 801 | "__________________________________________________________________________________________________\n", |
||
| 802 | "conv5_block6_0_relu (Activation (None, 4, 4, 672) 0 conv5_block6_0_bn[0][0] \n", |
||
| 803 | "__________________________________________________________________________________________________\n", |
||
| 804 | "conv5_block6_1_conv (Conv2D) (None, 4, 4, 128) 86016 conv5_block6_0_relu[0][0] \n", |
||
| 805 | "__________________________________________________________________________________________________\n", |
||
| 806 | "conv5_block6_1_bn (BatchNormali (None, 4, 4, 128) 512 conv5_block6_1_conv[0][0] \n", |
||
| 807 | "__________________________________________________________________________________________________\n", |
||
| 808 | "conv5_block6_1_relu (Activation (None, 4, 4, 128) 0 conv5_block6_1_bn[0][0] \n", |
||
| 809 | "__________________________________________________________________________________________________\n", |
||
| 810 | "conv5_block6_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block6_1_relu[0][0] \n", |
||
| 811 | "__________________________________________________________________________________________________\n", |
||
| 812 | "conv5_block6_concat (Concatenat (None, 4, 4, 704) 0 conv5_block5_concat[0][0] \n", |
||
| 813 | " conv5_block6_2_conv[0][0] \n", |
||
| 814 | "__________________________________________________________________________________________________\n", |
||
| 815 | "conv5_block7_0_bn (BatchNormali (None, 4, 4, 704) 2816 conv5_block6_concat[0][0] \n", |
||
| 816 | "__________________________________________________________________________________________________\n", |
||
| 817 | "conv5_block7_0_relu (Activation (None, 4, 4, 704) 0 conv5_block7_0_bn[0][0] \n", |
||
| 818 | "__________________________________________________________________________________________________\n", |
||
| 819 | "conv5_block7_1_conv (Conv2D) (None, 4, 4, 128) 90112 conv5_block7_0_relu[0][0] \n", |
||
| 820 | "__________________________________________________________________________________________________\n", |
||
| 821 | "conv5_block7_1_bn (BatchNormali (None, 4, 4, 128) 512 conv5_block7_1_conv[0][0] \n", |
||
| 822 | "__________________________________________________________________________________________________\n", |
||
| 823 | "conv5_block7_1_relu (Activation (None, 4, 4, 128) 0 conv5_block7_1_bn[0][0] \n", |
||
| 824 | "__________________________________________________________________________________________________\n", |
||
| 825 | "conv5_block7_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block7_1_relu[0][0] \n", |
||
| 826 | "__________________________________________________________________________________________________\n", |
||
| 827 | "conv5_block7_concat (Concatenat (None, 4, 4, 736) 0 conv5_block6_concat[0][0] \n", |
||
| 828 | " conv5_block7_2_conv[0][0] \n", |
||
| 829 | "__________________________________________________________________________________________________\n", |
||
| 830 | "conv5_block8_0_bn (BatchNormali (None, 4, 4, 736) 2944 conv5_block7_concat[0][0] \n", |
||
| 831 | "__________________________________________________________________________________________________\n", |
||
| 832 | "conv5_block8_0_relu (Activation (None, 4, 4, 736) 0 conv5_block8_0_bn[0][0] \n", |
||
| 833 | "__________________________________________________________________________________________________\n", |
||
| 834 | "conv5_block8_1_conv (Conv2D) (None, 4, 4, 128) 94208 conv5_block8_0_relu[0][0] \n", |
||
| 835 | "__________________________________________________________________________________________________\n", |
||
| 836 | "conv5_block8_1_bn (BatchNormali (None, 4, 4, 128) 512 conv5_block8_1_conv[0][0] \n", |
||
| 837 | "__________________________________________________________________________________________________\n", |
||
| 838 | "conv5_block8_1_relu (Activation (None, 4, 4, 128) 0 conv5_block8_1_bn[0][0] \n", |
||
| 839 | "__________________________________________________________________________________________________\n", |
||
| 840 | "conv5_block8_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block8_1_relu[0][0] \n", |
||
| 841 | "__________________________________________________________________________________________________\n", |
||
| 842 | "conv5_block8_concat (Concatenat (None, 4, 4, 768) 0 conv5_block7_concat[0][0] \n", |
||
| 843 | " conv5_block8_2_conv[0][0] \n", |
||
| 844 | "__________________________________________________________________________________________________\n", |
||
| 845 | "conv5_block9_0_bn (BatchNormali (None, 4, 4, 768) 3072 conv5_block8_concat[0][0] \n", |
||
| 846 | "__________________________________________________________________________________________________\n", |
||
| 847 | "conv5_block9_0_relu (Activation (None, 4, 4, 768) 0 conv5_block9_0_bn[0][0] \n", |
||
| 848 | "__________________________________________________________________________________________________\n", |
||
| 849 | "conv5_block9_1_conv (Conv2D) (None, 4, 4, 128) 98304 conv5_block9_0_relu[0][0] \n", |
||
| 850 | "__________________________________________________________________________________________________\n", |
||
| 851 | "conv5_block9_1_bn (BatchNormali (None, 4, 4, 128) 512 conv5_block9_1_conv[0][0] \n", |
||
| 852 | "__________________________________________________________________________________________________\n", |
||
| 853 | "conv5_block9_1_relu (Activation (None, 4, 4, 128) 0 conv5_block9_1_bn[0][0] \n", |
||
| 854 | "__________________________________________________________________________________________________\n", |
||
| 855 | "conv5_block9_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block9_1_relu[0][0] \n", |
||
| 856 | "__________________________________________________________________________________________________\n", |
||
| 857 | "conv5_block9_concat (Concatenat (None, 4, 4, 800) 0 conv5_block8_concat[0][0] \n", |
||
| 858 | " conv5_block9_2_conv[0][0] \n", |
||
| 859 | "__________________________________________________________________________________________________\n", |
||
| 860 | "conv5_block10_0_bn (BatchNormal (None, 4, 4, 800) 3200 conv5_block9_concat[0][0] \n", |
||
| 861 | "__________________________________________________________________________________________________\n", |
||
| 862 | "conv5_block10_0_relu (Activatio (None, 4, 4, 800) 0 conv5_block10_0_bn[0][0] \n", |
||
| 863 | "__________________________________________________________________________________________________\n", |
||
| 864 | "conv5_block10_1_conv (Conv2D) (None, 4, 4, 128) 102400 conv5_block10_0_relu[0][0] \n", |
||
| 865 | "__________________________________________________________________________________________________\n", |
||
| 866 | "conv5_block10_1_bn (BatchNormal (None, 4, 4, 128) 512 conv5_block10_1_conv[0][0] \n", |
||
| 867 | "__________________________________________________________________________________________________\n", |
||
| 868 | "conv5_block10_1_relu (Activatio (None, 4, 4, 128) 0 conv5_block10_1_bn[0][0] \n", |
||
| 869 | "__________________________________________________________________________________________________\n", |
||
| 870 | "conv5_block10_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block10_1_relu[0][0] \n", |
||
| 871 | "__________________________________________________________________________________________________\n", |
||
| 872 | "conv5_block10_concat (Concatena (None, 4, 4, 832) 0 conv5_block9_concat[0][0] \n", |
||
| 873 | " conv5_block10_2_conv[0][0] \n", |
||
| 874 | "__________________________________________________________________________________________________\n", |
||
| 875 | "conv5_block11_0_bn (BatchNormal (None, 4, 4, 832) 3328 conv5_block10_concat[0][0] \n", |
||
| 876 | "__________________________________________________________________________________________________\n", |
||
| 877 | "conv5_block11_0_relu (Activatio (None, 4, 4, 832) 0 conv5_block11_0_bn[0][0] \n", |
||
| 878 | "__________________________________________________________________________________________________\n", |
||
| 879 | "conv5_block11_1_conv (Conv2D) (None, 4, 4, 128) 106496 conv5_block11_0_relu[0][0] \n", |
||
| 880 | "__________________________________________________________________________________________________\n", |
||
| 881 | "conv5_block11_1_bn (BatchNormal (None, 4, 4, 128) 512 conv5_block11_1_conv[0][0] \n", |
||
| 882 | "__________________________________________________________________________________________________\n", |
||
| 883 | "conv5_block11_1_relu (Activatio (None, 4, 4, 128) 0 conv5_block11_1_bn[0][0] \n", |
||
| 884 | "__________________________________________________________________________________________________\n", |
||
| 885 | "conv5_block11_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block11_1_relu[0][0] \n", |
||
| 886 | "__________________________________________________________________________________________________\n", |
||
| 887 | "conv5_block11_concat (Concatena (None, 4, 4, 864) 0 conv5_block10_concat[0][0] \n", |
||
| 888 | " conv5_block11_2_conv[0][0] \n", |
||
| 889 | "__________________________________________________________________________________________________\n", |
||
| 890 | "conv5_block12_0_bn (BatchNormal (None, 4, 4, 864) 3456 conv5_block11_concat[0][0] \n", |
||
| 891 | "__________________________________________________________________________________________________\n", |
||
| 892 | "conv5_block12_0_relu (Activatio (None, 4, 4, 864) 0 conv5_block12_0_bn[0][0] \n", |
||
| 893 | "__________________________________________________________________________________________________\n", |
||
| 894 | "conv5_block12_1_conv (Conv2D) (None, 4, 4, 128) 110592 conv5_block12_0_relu[0][0] \n", |
||
| 895 | "__________________________________________________________________________________________________\n", |
||
| 896 | "conv5_block12_1_bn (BatchNormal (None, 4, 4, 128) 512 conv5_block12_1_conv[0][0] \n", |
||
| 897 | "__________________________________________________________________________________________________\n", |
||
| 898 | "conv5_block12_1_relu (Activatio (None, 4, 4, 128) 0 conv5_block12_1_bn[0][0] \n", |
||
| 899 | "__________________________________________________________________________________________________\n", |
||
| 900 | "conv5_block12_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block12_1_relu[0][0] \n", |
||
| 901 | "__________________________________________________________________________________________________\n", |
||
| 902 | "conv5_block12_concat (Concatena (None, 4, 4, 896) 0 conv5_block11_concat[0][0] \n", |
||
| 903 | " conv5_block12_2_conv[0][0] \n", |
||
| 904 | "__________________________________________________________________________________________________\n", |
||
| 905 | "conv5_block13_0_bn (BatchNormal (None, 4, 4, 896) 3584 conv5_block12_concat[0][0] \n", |
||
| 906 | "__________________________________________________________________________________________________\n", |
||
| 907 | "conv5_block13_0_relu (Activatio (None, 4, 4, 896) 0 conv5_block13_0_bn[0][0] \n", |
||
| 908 | "__________________________________________________________________________________________________\n", |
||
| 909 | "conv5_block13_1_conv (Conv2D) (None, 4, 4, 128) 114688 conv5_block13_0_relu[0][0] \n", |
||
| 910 | "__________________________________________________________________________________________________\n", |
||
| 911 | "conv5_block13_1_bn (BatchNormal (None, 4, 4, 128) 512 conv5_block13_1_conv[0][0] \n", |
||
| 912 | "__________________________________________________________________________________________________\n", |
||
| 913 | "conv5_block13_1_relu (Activatio (None, 4, 4, 128) 0 conv5_block13_1_bn[0][0] \n", |
||
| 914 | "__________________________________________________________________________________________________\n", |
||
| 915 | "conv5_block13_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block13_1_relu[0][0] \n", |
||
| 916 | "__________________________________________________________________________________________________\n", |
||
| 917 | "conv5_block13_concat (Concatena (None, 4, 4, 928) 0 conv5_block12_concat[0][0] \n", |
||
| 918 | " conv5_block13_2_conv[0][0] \n", |
||
| 919 | "__________________________________________________________________________________________________\n", |
||
| 920 | "conv5_block14_0_bn (BatchNormal (None, 4, 4, 928) 3712 conv5_block13_concat[0][0] \n", |
||
| 921 | "__________________________________________________________________________________________________\n", |
||
| 922 | "conv5_block14_0_relu (Activatio (None, 4, 4, 928) 0 conv5_block14_0_bn[0][0] \n", |
||
| 923 | "__________________________________________________________________________________________________\n", |
||
| 924 | "conv5_block14_1_conv (Conv2D) (None, 4, 4, 128) 118784 conv5_block14_0_relu[0][0] \n", |
||
| 925 | "__________________________________________________________________________________________________\n", |
||
| 926 | "conv5_block14_1_bn (BatchNormal (None, 4, 4, 128) 512 conv5_block14_1_conv[0][0] \n", |
||
| 927 | "__________________________________________________________________________________________________\n", |
||
| 928 | "conv5_block14_1_relu (Activatio (None, 4, 4, 128) 0 conv5_block14_1_bn[0][0] \n", |
||
| 929 | "__________________________________________________________________________________________________\n", |
||
| 930 | "conv5_block14_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block14_1_relu[0][0] \n", |
||
| 931 | "__________________________________________________________________________________________________\n", |
||
| 932 | "conv5_block14_concat (Concatena (None, 4, 4, 960) 0 conv5_block13_concat[0][0] \n", |
||
| 933 | " conv5_block14_2_conv[0][0] \n", |
||
| 934 | "__________________________________________________________________________________________________\n", |
||
| 935 | "conv5_block15_0_bn (BatchNormal (None, 4, 4, 960) 3840 conv5_block14_concat[0][0] \n", |
||
| 936 | "__________________________________________________________________________________________________\n", |
||
| 937 | "conv5_block15_0_relu (Activatio (None, 4, 4, 960) 0 conv5_block15_0_bn[0][0] \n", |
||
| 938 | "__________________________________________________________________________________________________\n", |
||
| 939 | "conv5_block15_1_conv (Conv2D) (None, 4, 4, 128) 122880 conv5_block15_0_relu[0][0] \n", |
||
| 940 | "__________________________________________________________________________________________________\n", |
||
| 941 | "conv5_block15_1_bn (BatchNormal (None, 4, 4, 128) 512 conv5_block15_1_conv[0][0] \n", |
||
| 942 | "__________________________________________________________________________________________________\n", |
||
| 943 | "conv5_block15_1_relu (Activatio (None, 4, 4, 128) 0 conv5_block15_1_bn[0][0] \n", |
||
| 944 | "__________________________________________________________________________________________________\n", |
||
| 945 | "conv5_block15_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block15_1_relu[0][0] \n", |
||
| 946 | "__________________________________________________________________________________________________\n", |
||
| 947 | "conv5_block15_concat (Concatena (None, 4, 4, 992) 0 conv5_block14_concat[0][0] \n", |
||
| 948 | " conv5_block15_2_conv[0][0] \n", |
||
| 949 | "__________________________________________________________________________________________________\n", |
||
| 950 | "conv5_block16_0_bn (BatchNormal (None, 4, 4, 992) 3968 conv5_block15_concat[0][0] \n", |
||
| 951 | "__________________________________________________________________________________________________\n", |
||
| 952 | "conv5_block16_0_relu (Activatio (None, 4, 4, 992) 0 conv5_block16_0_bn[0][0] \n", |
||
| 953 | "__________________________________________________________________________________________________\n", |
||
| 954 | "conv5_block16_1_conv (Conv2D) (None, 4, 4, 128) 126976 conv5_block16_0_relu[0][0] \n", |
||
| 955 | "__________________________________________________________________________________________________\n", |
||
| 956 | "conv5_block16_1_bn (BatchNormal (None, 4, 4, 128) 512 conv5_block16_1_conv[0][0] \n", |
||
| 957 | "__________________________________________________________________________________________________\n", |
||
| 958 | "conv5_block16_1_relu (Activatio (None, 4, 4, 128) 0 conv5_block16_1_bn[0][0] \n", |
||
| 959 | "__________________________________________________________________________________________________\n", |
||
| 960 | "conv5_block16_2_conv (Conv2D) (None, 4, 4, 32) 36864 conv5_block16_1_relu[0][0] \n", |
||
| 961 | "__________________________________________________________________________________________________\n", |
||
| 962 | "conv5_block16_concat (Concatena (None, 4, 4, 1024) 0 conv5_block15_concat[0][0] \n", |
||
| 963 | " conv5_block16_2_conv[0][0] \n", |
||
| 964 | "__________________________________________________________________________________________________\n", |
||
| 965 | "bn (BatchNormalization) (None, 4, 4, 1024) 4096 conv5_block16_concat[0][0] \n", |
||
| 966 | "__________________________________________________________________________________________________\n", |
||
| 967 | "relu (Activation) (None, 4, 4, 1024) 0 bn[0][0] \n", |
||
| 968 | "__________________________________________________________________________________________________\n", |
||
| 969 | "flatten_12 (Flatten) (None, 16384) 0 relu[0][0] \n", |
||
| 970 | "__________________________________________________________________________________________________\n", |
||
| 971 | "dense_24 (Dense) (None, 1024) 16778240 flatten_12[0][0] \n", |
||
| 972 | "__________________________________________________________________________________________________\n", |
||
| 973 | "dense_25 (Dense) (None, 1) 1025 dense_24[0][0] \n", |
||
| 974 | "==================================================================================================\n", |
||
| 975 | "Total params: 23,816,769\n", |
||
| 976 | "Trainable params: 23,733,121\n", |
||
| 977 | "Non-trainable params: 83,648\n", |
||
| 978 | "__________________________________________________________________________________________________\n", |
||
| 979 | "dense_25\n" |
||
| 980 | ] |
||
| 981 | }, |
||
| 982 | { |
||
| 983 | "name": "stdout", |
||
| 984 | "output_type": "stream", |
||
| 985 | "text": [ |
||
| 986 | "Done\n", |
||
| 987 | "30\n" |
||
| 988 | ] |
||
| 989 | }, |
||
| 990 | { |
||
| 991 | "name": "stderr", |
||
| 992 | "output_type": "stream", |
||
| 993 | "text": [ |
||
| 994 | "Lossy conversion from float32 to uint8. Range [26.0, 188.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
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| 995 | "Lossy conversion from float32 to uint8. Range [26.0, 176.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
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| 996 | "Lossy conversion from float32 to uint8. Range [38.0, 197.0]. Convert image to uint8 prior to saving to suppress this warning.\n" |
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| 997 | ] |
||
| 998 | }, |
||
| 999 | { |
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| 1000 | "name": "stdout", |
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| 1001 | "output_type": "stream", |
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| 1002 | "text": [ |
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| 1003 | "Im132_0.tif [[0.99999213]]\n", |
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| 1004 | "Im136_0.tif [[0.9999312]]\n", |
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| 1005 | "Im148_0.tif [[0.99993384]]\n", |
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| 1006 | "Im152_0.tif [[0.9999883]]\n" |
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| 1007 | ] |
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| 1008 | }, |
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| 1009 | { |
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| 1010 | "name": "stderr", |
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| 1011 | "output_type": "stream", |
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| 1012 | "text": [ |
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| 1013 | "Lossy conversion from float32 to uint8. Range [20.0, 180.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
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| 1014 | "Lossy conversion from float32 to uint8. Range [27.0, 197.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
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| 1015 | "Lossy conversion from float32 to uint8. Range [23.0, 187.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
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| 1016 | "Lossy conversion from float32 to uint8. Range [35.0, 182.0]. Convert image to uint8 prior to saving to suppress this warning.\n" |
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| 1017 | ] |
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| 1018 | }, |
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| 1019 | { |
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| 1020 | "name": "stdout", |
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| 1021 | "output_type": "stream", |
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| 1022 | "text": [ |
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| 1023 | "Im156_0.tif [[0.99999475]]\n", |
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| 1024 | "Im165_0.tif [[0.9999957]]\n", |
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| 1025 | "Im171_0.tif [[0.9999974]]\n", |
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| 1026 | "Im172_0.tif [[0.99989057]]\n" |
||
| 1027 | ] |
||
| 1028 | }, |
||
| 1029 | { |
||
| 1030 | "name": "stderr", |
||
| 1031 | "output_type": "stream", |
||
| 1032 | "text": [ |
||
| 1033 | "Lossy conversion from float32 to uint8. Range [36.0, 192.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1034 | "Lossy conversion from float32 to uint8. Range [17.0, 182.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1035 | "Lossy conversion from float32 to uint8. Range [40.0, 175.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1036 | "Lossy conversion from float32 to uint8. Range [35.0, 175.0]. Convert image to uint8 prior to saving to suppress this warning.\n" |
||
| 1037 | ] |
||
| 1038 | }, |
||
| 1039 | { |
||
| 1040 | "name": "stdout", |
||
| 1041 | "output_type": "stream", |
||
| 1042 | "text": [ |
||
| 1043 | "Im176_0.tif [[0.9998524]]\n", |
||
| 1044 | "Im178_0.tif [[0.9999939]]\n", |
||
| 1045 | "Im181_0.tif [[0.99999845]]\n", |
||
| 1046 | "Im187_0.tif [[0.99999]]\n" |
||
| 1047 | ] |
||
| 1048 | }, |
||
| 1049 | { |
||
| 1050 | "name": "stderr", |
||
| 1051 | "output_type": "stream", |
||
| 1052 | "text": [ |
||
| 1053 | "Lossy conversion from float32 to uint8. Range [25.0, 191.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1054 | "Lossy conversion from float32 to uint8. Range [24.0, 193.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1055 | "Lossy conversion from float32 to uint8. Range [24.0, 189.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1056 | "Lossy conversion from float32 to uint8. Range [19.0, 166.0]. Convert image to uint8 prior to saving to suppress this warning.\n" |
||
| 1057 | ] |
||
| 1058 | }, |
||
| 1059 | { |
||
| 1060 | "name": "stdout", |
||
| 1061 | "output_type": "stream", |
||
| 1062 | "text": [ |
||
| 1063 | "Im189_0.tif [[0.9998784]]\n", |
||
| 1064 | "Im191_0.tif [[0.99999464]]\n", |
||
| 1065 | "Im195_0.tif [[0.999946]]\n", |
||
| 1066 | "Im201_0.tif [[0.9999964]]\n" |
||
| 1067 | ] |
||
| 1068 | }, |
||
| 1069 | { |
||
| 1070 | "name": "stderr", |
||
| 1071 | "output_type": "stream", |
||
| 1072 | "text": [ |
||
| 1073 | "Lossy conversion from float32 to uint8. Range [22.0, 175.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1074 | "Lossy conversion from float32 to uint8. Range [24.0, 178.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1075 | "Lossy conversion from float32 to uint8. Range [21.0, 181.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1076 | "Lossy conversion from float32 to uint8. Range [18.0, 173.0]. Convert image to uint8 prior to saving to suppress this warning.\n" |
||
| 1077 | ] |
||
| 1078 | }, |
||
| 1079 | { |
||
| 1080 | "name": "stdout", |
||
| 1081 | "output_type": "stream", |
||
| 1082 | "text": [ |
||
| 1083 | "Im212_0.tif [[0.9998431]]\n", |
||
| 1084 | "Im218_0.tif [[0.9999825]]\n", |
||
| 1085 | "Im222_0.tif [[0.9998821]]\n" |
||
| 1086 | ] |
||
| 1087 | }, |
||
| 1088 | { |
||
| 1089 | "name": "stderr", |
||
| 1090 | "output_type": "stream", |
||
| 1091 | "text": [ |
||
| 1092 | "Lossy conversion from float32 to uint8. Range [28.0, 170.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1093 | "Lossy conversion from float32 to uint8. Range [13.0, 119.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1094 | "Lossy conversion from float32 to uint8. Range [16.0, 142.0]. Convert image to uint8 prior to saving to suppress this warning.\n" |
||
| 1095 | ] |
||
| 1096 | }, |
||
| 1097 | { |
||
| 1098 | "name": "stdout", |
||
| 1099 | "output_type": "stream", |
||
| 1100 | "text": [ |
||
| 1101 | "Im226_0.tif [[0.999998]]\n", |
||
| 1102 | "Im228_0.tif [[0.9999784]]\n", |
||
| 1103 | "Im232_0.tif [[0.9999753]]\n" |
||
| 1104 | ] |
||
| 1105 | }, |
||
| 1106 | { |
||
| 1107 | "name": "stderr", |
||
| 1108 | "output_type": "stream", |
||
| 1109 | "text": [ |
||
| 1110 | "Lossy conversion from float32 to uint8. Range [22.0, 146.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1111 | "Lossy conversion from float32 to uint8. Range [21.0, 146.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1112 | "Lossy conversion from float32 to uint8. Range [19.0, 143.0]. Convert image to uint8 prior to saving to suppress this warning.\n" |
||
| 1113 | ] |
||
| 1114 | }, |
||
| 1115 | { |
||
| 1116 | "name": "stdout", |
||
| 1117 | "output_type": "stream", |
||
| 1118 | "text": [ |
||
| 1119 | "Im243_0.tif [[0.9999944]]\n", |
||
| 1120 | "Im246_0.tif [[0.9999696]]\n", |
||
| 1121 | "Im250_0.tif [[0.999811]]\n", |
||
| 1122 | "Im251_0.tif [[0.9998184]]\n" |
||
| 1123 | ] |
||
| 1124 | }, |
||
| 1125 | { |
||
| 1126 | "name": "stderr", |
||
| 1127 | "output_type": "stream", |
||
| 1128 | "text": [ |
||
| 1129 | "Lossy conversion from float32 to uint8. Range [15.0, 127.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1130 | "Lossy conversion from float32 to uint8. Range [19.0, 144.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1131 | "Lossy conversion from float32 to uint8. Range [26.0, 138.0]. Convert image to uint8 prior to saving to suppress this warning.\n", |
||
| 1132 | "Lossy conversion from float32 to uint8. Range [17.0, 175.0]. Convert image to uint8 prior to saving to suppress this warning.\n" |
||
| 1133 | ] |
||
| 1134 | }, |
||
| 1135 | { |
||
| 1136 | "name": "stdout", |
||
| 1137 | "output_type": "stream", |
||
| 1138 | "text": [ |
||
| 1139 | "Im252_0.tif [[0.9999641]]\n", |
||
| 1140 | "Im254_0.tif [[0.9985845]]\n", |
||
| 1141 | "Im257_0.tif [[0.99995434]]\n", |
||
| 1142 | "Im260_0.tif [[0.9999981]]\n" |
||
| 1143 | ] |
||
| 1144 | }, |
||
| 1145 | { |
||
| 1146 | "name": "stderr", |
||
| 1147 | "output_type": "stream", |
||
| 1148 | "text": [ |
||
| 1149 | "Lossy conversion from float32 to uint8. Range [28.0, 165.0]. Convert image to uint8 prior to saving to suppress this warning.\n" |
||
| 1150 | ] |
||
| 1151 | }, |
||
| 1152 | { |
||
| 1153 | "name": "stdout", |
||
| 1154 | "output_type": "stream", |
||
| 1155 | "text": [ |
||
| 1156 | "Number of 0 : 0\n", |
||
| 1157 | "Number of 1 : 30\n" |
||
| 1158 | ] |
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| 1159 | }, |
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| 1160 | { |
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| 1161 | "data": { |
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", |
||
| 1163 | "text/plain": [ |
||
| 1164 | "<Figure size 432x288 with 1 Axes>" |
||
| 1165 | ] |
||
| 1166 | }, |
||
| 1167 | "metadata": { |
||
| 1168 | "needs_background": "light" |
||
| 1169 | }, |
||
| 1170 | "output_type": "display_data" |
||
| 1171 | } |
||
| 1172 | ], |
||
| 1173 | "source": [ |
||
| 1174 | "import tensorflow as tf \n", |
||
| 1175 | "physical_devices = tf.config.list_physical_devices('GPU') \n", |
||
| 1176 | "tf.config.experimental.set_memory_growth(physical_devices[0], True)\n", |
||
| 1177 | "\n", |
||
| 1178 | "\n", |
||
| 1179 | "import numpy as np\n", |
||
| 1180 | "import tensorflow as tf\n", |
||
| 1181 | "from tensorflow import keras\n", |
||
| 1182 | "\n", |
||
| 1183 | "# Display\n", |
||
| 1184 | "from IPython.display import Image, display\n", |
||
| 1185 | "import matplotlib.pyplot as plt\n", |
||
| 1186 | "import matplotlib.cm as cm\n", |
||
| 1187 | "\n", |
||
| 1188 | "import numpy as np\n", |
||
| 1189 | "import os\n", |
||
| 1190 | "import cv2\n", |
||
| 1191 | "import matplotlib.pyplot as plt\n", |
||
| 1192 | "%matplotlib inline\n", |
||
| 1193 | "import operator\n", |
||
| 1194 | "import tensorflow as tf\n", |
||
| 1195 | "import random\n", |
||
| 1196 | "import skimage\n", |
||
| 1197 | "from skimage.io import imread, imshow\n", |
||
| 1198 | "\n", |
||
| 1199 | "\n", |
||
| 1200 | "def crop_center(img, bounding):\n", |
||
| 1201 | " start = tuple(map(lambda a, da: a//2-da//2, img.shape, bounding))\n", |
||
| 1202 | " end = tuple(map(operator.add, start, bounding))\n", |
||
| 1203 | " slices = tuple(map(slice, start, end))\n", |
||
| 1204 | " im = img[slices].astype('float32')\n", |
||
| 1205 | " return im\n", |
||
| 1206 | "\n", |
||
| 1207 | "VAL_ALL_PATH = r'F:\\Leuk study re-designed\\ALLIDB-2 best models\\High\\Test\\all'\n", |
||
| 1208 | "VAL_HEM_PATH = r'F:\\Leuk study re-designed\\ALLIDB-2 best models\\High\\Test\\hem'\n", |
||
| 1209 | "\n", |
||
| 1210 | "# VAL_ALL_PATH = r'F:\\Leuk study re-designed\\C-NMC best model\\High\\Test enhanched\\all'\n", |
||
| 1211 | "# VAL_HEM_PATH = r'F:\\Leuk study re-designed\\C-NMC best model\\High\\Test enhanched\\hem'\n", |
||
| 1212 | "\n", |
||
| 1213 | "all_list = os.listdir(VAL_ALL_PATH) \n", |
||
| 1214 | "hem_list = os.listdir(VAL_HEM_PATH)\n", |
||
| 1215 | "\n", |
||
| 1216 | "all_list.sort()\n", |
||
| 1217 | "hem_list.sort()\n", |
||
| 1218 | "\n", |
||
| 1219 | "\n", |
||
| 1220 | "'''FIX THESE PARAMETER'''\n", |
||
| 1221 | "# PATH = VAL_ALL_PATH\n", |
||
| 1222 | "# LIST = all_list\n", |
||
| 1223 | "\n", |
||
| 1224 | "PATH = VAL_HEM_PATH\n", |
||
| 1225 | "LIST = hem_list\n", |
||
| 1226 | "\n", |
||
| 1227 | "# root_dir = r'F:\\Leuk study re-designed\\ALLIDB-2 best models\\High' # ALLIDB-2 High root\n", |
||
| 1228 | "root_dir = r'F:\\Leuk study re-designed\\ALLIDB-2 best models\\Low' # ALLIDB-2 Low root\n", |
||
| 1229 | "\n", |
||
| 1230 | "# root_dir = r'F:\\Leuk study re-designed\\C-NMC best model\\High' # C-NMC High root\n", |
||
| 1231 | "# root_dir = r'F:\\Leuk study re-designed\\C-NMC best model\\Low' # C-NMC Low root\n", |
||
| 1232 | "\n", |
||
| 1233 | "# model_name = r'HRD_EfficientNetB0_MccLoss_test1.h5' # C-NMC High model\n", |
||
| 1234 | "# model_name = r'LRD_DesnseNet121_weighted_test1.h5' # C-NMC Low model\n", |
||
| 1235 | "\n", |
||
| 1236 | "# model_name = r'EffNetB0_mcc_loss_high.h5' # ALLIDB-2 High model\n", |
||
| 1237 | "model_name = r'DenseNet121_low_class_weight.h5' # ALLIDB-2 Low model\n", |
||
| 1238 | "\n", |
||
| 1239 | "# last_conv_layer_name = \"top_activation\" # For EfficientNetBo\n", |
||
| 1240 | "last_conv_layer_name = \"conv5_block16_concat\" #For densenet121\n", |
||
| 1241 | "\n", |
||
| 1242 | "out_dir = r'F:\\Leuk study re-designed\\ALLIDB-2 best models\\Low\\corresponding hetmaps\\hem'\n", |
||
| 1243 | "index = 0 #Index of ouput layer 0 = all,, 1 = hem\n", |
||
| 1244 | "\n", |
||
| 1245 | "crop_height = 150\n", |
||
| 1246 | "crop_width = 150\n", |
||
| 1247 | "'''END'''\n", |
||
| 1248 | "\n", |
||
| 1249 | "\n", |
||
| 1250 | "\n", |
||
| 1251 | "import os\n", |
||
| 1252 | "import skimage \n", |
||
| 1253 | "from skimage.io import imread, imshow\n", |
||
| 1254 | "img_all = imread(os.path.join(PATH, LIST[0]))\n", |
||
| 1255 | "# img_all = imread(r'F:\\Leuk study re-designed\\C-NMC best model\\High\\Test enhanched\\hem\\4.bmp')\n", |
||
| 1256 | "print('MAx: ', np.max(img_all))\n", |
||
| 1257 | "print('MIN: ', np.min(img_all))\n", |
||
| 1258 | "cropped_img_all = crop_center(img_all, (crop_height,crop_width,3))\n", |
||
| 1259 | "print('Cropped_img MAx: ', np.max(cropped_img_all))\n", |
||
| 1260 | "print('Cropped_img MIN: ', np.min(cropped_img_all))\n", |
||
| 1261 | "cropped_img_all = cropped_img_all.astype('uint8')\n", |
||
| 1262 | "print(cropped_img_all.dtype)\n", |
||
| 1263 | "imshow(cropped_img_all)\n", |
||
| 1264 | "rescaled_cropped_img_all = cropped_img_all * (1.0/255.0)\n", |
||
| 1265 | "print('Rescaled_cropped_img MAx: ', np.max(rescaled_cropped_img_all))\n", |
||
| 1266 | "print('Resclased_cropped_img MIN: ', np.min(rescaled_cropped_img_all))\n", |
||
| 1267 | "print(type(rescaled_cropped_img_all))\n", |
||
| 1268 | "print(rescaled_cropped_img_all.shape)\n", |
||
| 1269 | "print(rescaled_cropped_img_all.dtype)\n", |
||
| 1270 | "array = np.expand_dims(rescaled_cropped_img_all, axis=0)\n", |
||
| 1271 | "print(array.dtype, array.shape)\n", |
||
| 1272 | "\n", |
||
| 1273 | "\n", |
||
| 1274 | "from tensorflow import keras\n", |
||
| 1275 | "# root_dir = r'F:\\Leuk study re-designed\\ALLIDB-2 best models\\High'\n", |
||
| 1276 | "# model_name = r'EffNetB0_mcc_loss_high.h5'\n", |
||
| 1277 | "'''loading model'''\n", |
||
| 1278 | "# model = model.load_weights(os.path.join(root_dir, model_name))\n", |
||
| 1279 | "model = keras.models.load_model(os.path.join(root_dir, model_name), compile=False)\n", |
||
| 1280 | "model.summary()\n", |
||
| 1281 | "print(model.layers[-1].name)\n", |
||
| 1282 | "# last_conv_layer_name = \"conv5_block16_concat\" # Foe EfficientB0\n", |
||
| 1283 | "# last_conv_layer_name = \"conv5_block16_concat\" #For Densenet121\n", |
||
| 1284 | "\n", |
||
| 1285 | "\n", |
||
| 1286 | "#pip install tf-explain\n", |
||
| 1287 | "\n", |
||
| 1288 | "\n", |
||
| 1289 | "\n", |
||
| 1290 | "\n", |
||
| 1291 | "\n", |
||
| 1292 | "# Import explainer\n", |
||
| 1293 | "import tf_explain\n", |
||
| 1294 | "from tf_explain.core.grad_cam import GradCAM\n", |
||
| 1295 | "\n", |
||
| 1296 | "# Instantiation of the explainer\n", |
||
| 1297 | "explainer = GradCAM()\n", |
||
| 1298 | "\n", |
||
| 1299 | "arr = array[0,:,:,:]\n", |
||
| 1300 | "data = ([arr], None)\n", |
||
| 1301 | "# Call to explain() method\n", |
||
| 1302 | "output = explainer.explain(data, model, class_index=index, layer_name=last_conv_layer_name, colormap=cv2.COLORMAP_JET)\n", |
||
| 1303 | "\n", |
||
| 1304 | "# output_dir = r'D:\\new_leuk\\Leukemia_Work_Revive\\Corrected\\Grad-cam heatmap'\n", |
||
| 1305 | "# output_name = 'test2.png'\n", |
||
| 1306 | "\n", |
||
| 1307 | "# Save output\n", |
||
| 1308 | "#explainer.save(output, output_dir, output_name)\n", |
||
| 1309 | "\n", |
||
| 1310 | "for x in range(len(LIST)):\n", |
||
| 1311 | " img = imread(os.path.join(PATH, LIST[x]))\n", |
||
| 1312 | " cropped_img = crop_center(img, (crop_height,crop_width,3))\n", |
||
| 1313 | " rescaled_cropped_img = cropped_img * (1./255.)\n", |
||
| 1314 | " data = ([rescaled_cropped_img], None)\n", |
||
| 1315 | " output = explainer.explain(data, model, class_index=index, layer_name=last_conv_layer_name, colormap=cv2.COLORMAP_JET)\n", |
||
| 1316 | " output_dir = out_dir\n", |
||
| 1317 | " output_name = LIST[x]\n", |
||
| 1318 | " output_name = output_name[:-4]\n", |
||
| 1319 | " output_name = output_name + '.png'\n", |
||
| 1320 | " explainer.save(output, output_dir, output_name)\n", |
||
| 1321 | "\n", |
||
| 1322 | "print(\"Done\")\n", |
||
| 1323 | "\n", |
||
| 1324 | "\n", |
||
| 1325 | "\n", |
||
| 1326 | "\n", |
||
| 1327 | "import os\n", |
||
| 1328 | "import numpy as np\n", |
||
| 1329 | "import skimage\n", |
||
| 1330 | "from skimage.io import imshow, imread, imsave\n", |
||
| 1331 | "print(len(LIST))\n", |
||
| 1332 | "pred_list = []\n", |
||
| 1333 | "for x in LIST:\n", |
||
| 1334 | " single_orig_image = imread(os.path.join(PATH, x))\n", |
||
| 1335 | " single_cropped_img = crop_center(single_orig_image, (crop_height,crop_width,3))\n", |
||
| 1336 | " single_cropped_img_dim_extended = np.expand_dims(single_cropped_img, axis=0)\n", |
||
| 1337 | " single_cropped_img_dim_extended = single_cropped_img_dim_extended / 255.0\n", |
||
| 1338 | " pred_value = model.predict(single_cropped_img_dim_extended)\n", |
||
| 1339 | " print(x, pred_value)\n", |
||
| 1340 | " if pred_value > 0.5:\n", |
||
| 1341 | " pred_list.append(1)\n", |
||
| 1342 | " pred_flat = 1\n", |
||
| 1343 | " else:\n", |
||
| 1344 | " pred_list.append(0)\n", |
||
| 1345 | " pred_flat = 0\n", |
||
| 1346 | " des_path = out_dir + '/' + x[:-4] + '_' + str(pred_flat) + '.png'\n", |
||
| 1347 | " imsave(des_path, single_cropped_img)\n", |
||
| 1348 | " \n", |
||
| 1349 | "\n", |
||
| 1350 | "print('Number of 0 :', pred_list.count(0))\n", |
||
| 1351 | "print('Number of 1 :', pred_list.count(1))" |
||
| 1352 | ] |
||
| 1353 | }, |
||
| 1354 | { |
||
| 1355 | "cell_type": "markdown", |
||
| 1356 | "id": "1ebc6f94", |
||
| 1357 | "metadata": {}, |
||
| 1358 | "source": [ |
||
| 1359 | "# Split classifed and misclassified" |
||
| 1360 | ] |
||
| 1361 | }, |
||
| 1362 | { |
||
| 1363 | "cell_type": "code", |
||
| 1364 | "execution_count": 38, |
||
| 1365 | "id": "7175982d", |
||
| 1366 | "metadata": {}, |
||
| 1367 | "outputs": [ |
||
| 1368 | { |
||
| 1369 | "data": { |
||
| 1370 | "text/plain": [ |
||
| 1371 | "\"\\ntarget_path0 = r'F:\\\\Leuk study re-designed\\\\C-NMC best model\\\\High\\\\corresponding heatmaps\\x07ll\\\\classsified'\\ntarget_path1 = r'F:\\\\Leuk study re-designed\\\\C-NMC best model\\\\High\\\\corresponding heatmaps\\x07ll\\\\misclasssified'\\n\\nfor x in range (len(all_images)):\\n if all_images[x][-5:-4] == 0:\\n orig = imread(os.path.join(path, all_images[x]))\\n final_path = target_path0 + '/' + all_images[x] \\n imsave(final_path, orig)\\n print(all_images[x][:-6])\\n name = all_images[x][:-6] + '.png'\\n print('name: ', name)\\n heatmap = imread(os.path.join(path, name))\\n final_path = target_path0 + '/' + all_images[x][:-6] + '.png'\\n imsave(final_path, heatmap)\\n else:\\n orig = imread(os.path.join(path, all_images[x]))\\n final_path = target_path1 + '/' + all_images[x] \\n imsave(final_path, orig)\\n print(all_images[x])\\n print(all_images[x][:-6])\\n name = all_images[x][:-6] + '.png'\\n print('name: ', name)\\n heatmap = imread(os.path.join(path, name))\\n final_path = target_path1 + '/' + all_images[x][:-6] + '.png'\\n imsave(final_path, heatmap)\\n \"" |
||
| 1372 | ] |
||
| 1373 | }, |
||
| 1374 | "execution_count": 38, |
||
| 1375 | "metadata": {}, |
||
| 1376 | "output_type": "execute_result" |
||
| 1377 | } |
||
| 1378 | ], |
||
| 1379 | "source": [ |
||
| 1380 | "import os\n", |
||
| 1381 | "\n", |
||
| 1382 | "path = r'F:\\Leuk study re-designed\\C-NMC best model\\High\\corresponding heatmaps\\all'\n", |
||
| 1383 | "all_images = os.listdir(path)\n", |
||
| 1384 | "all_images.sort()\n", |
||
| 1385 | "all_images\n", |
||
| 1386 | "\n", |
||
| 1387 | "target_path0 = r'F:\\Leuk study re-designed\\C-NMC best model\\High\\corresponding heatmaps\\all\\classsified'\n", |
||
| 1388 | "target_path1 = r'F:\\Leuk study re-designed\\C-NMC best model\\High\\corresponding heatmaps\\all\\misclasssified'\n", |
||
| 1389 | "\n", |
||
| 1390 | "for x in range (len(all_images)):\n", |
||
| 1391 | " if all_images[x][-5:-4] == 0:\n", |
||
| 1392 | " orig = imread(os.path.join(path, all_images[x]))\n", |
||
| 1393 | " final_path = target_path0 + '/' + all_images[x] \n", |
||
| 1394 | " imsave(final_path, orig)\n", |
||
| 1395 | " print(all_images[x][:-6])\n", |
||
| 1396 | " name = all_images[x][:-6] + '.png'\n", |
||
| 1397 | " print('name: ', name)\n", |
||
| 1398 | " heatmap = imread(os.path.join(path, name))\n", |
||
| 1399 | " final_path = target_path0 + '/' + all_images[x][:-6] + '.png'\n", |
||
| 1400 | " imsave(final_path, heatmap)\n", |
||
| 1401 | " else:\n", |
||
| 1402 | " orig = imread(os.path.join(path, all_images[x]))\n", |
||
| 1403 | " final_path = target_path1 + '/' + all_images[x] \n", |
||
| 1404 | " imsave(final_path, orig)\n", |
||
| 1405 | " print(all_images[x])\n", |
||
| 1406 | " print(all_images[x][:-6])\n", |
||
| 1407 | " name = all_images[x][:-6] + '.png'\n", |
||
| 1408 | " print('name: ', name)\n", |
||
| 1409 | " heatmap = imread(os.path.join(path, name))\n", |
||
| 1410 | " final_path = target_path1 + '/' + all_images[x][:-6] + '.png'\n", |
||
| 1411 | " imsave(final_path, heatmap)\n" |
||
| 1412 | ] |
||
| 1413 | } |
||
| 1414 | ], |
||
| 1415 | "metadata": { |
||
| 1416 | "kernelspec": { |
||
| 1417 | "display_name": "leukemia", |
||
| 1418 | "language": "python", |
||
| 1419 | "name": "leukemia" |
||
| 1420 | }, |
||
| 1421 | "language_info": { |
||
| 1422 | "codemirror_mode": { |
||
| 1423 | "name": "ipython", |
||
| 1424 | "version": 3 |
||
| 1425 | }, |
||
| 1426 | "file_extension": ".py", |
||
| 1427 | "mimetype": "text/x-python", |
||
| 1428 | "name": "python", |
||
| 1429 | "nbconvert_exporter": "python", |
||
| 1430 | "pygments_lexer": "ipython3", |
||
| 1431 | "version": "3.8.3" |
||
| 1432 | } |
||
| 1433 | }, |
||
| 1434 | "nbformat": 4, |
||
| 1435 | "nbformat_minor": 5 |
||
| 1436 | } |