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| a | b/ConvNet.py | ||
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| 1 | # -*- coding: utf-8 -*- |
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| 2 | """ |
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| 3 | Created on Wed Nov 02 11:36:23 2016 |
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| 4 | |||
| 5 | @author: seeker105 |
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| 6 | """ |
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| 7 | |||
| 8 | ''' First CNN training and testing ''' |
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| 9 | |||
| 10 | from keras.models import Sequential |
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| 11 | from keras.models import Model |
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| 12 | from keras.layers.advanced_activations import LeakyReLU |
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| 13 | from keras.layers import Input, merge, Convolution2D, MaxPooling2D |
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| 14 | from keras.layers import UpSampling2D, Activation, Dropout |
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| 15 | from keras.layers import Dense, Flatten, Reshape |
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| 16 | from keras.optimizers import Adam, SGD |
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| 17 | from keras.layers.normalization import BatchNormalization |
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| 18 | from keras.initializers import constant |
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| 19 | from keras.regularizers import l1_l2 |
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| 20 | from keras import backend as K |
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| 21 | from keras.engine.topology import Layer |
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| 22 | from theano.tensor.nnet.abstract_conv import bilinear_upsampling |
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| 23 | |||
| 24 | #bilinear upsampling layer |
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| 25 | class Deconv2D(Layer): |
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| 26 | def __init__(self, ratio, **kwargs): |
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| 27 | self.ratio = ratio |
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| 28 | super(Deconv2D, self).__init__(**kwargs) |
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| 29 | def build(self, input_shape): |
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| 30 | super(Deconv2D,self).build(input_shape) |
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| 31 | def call(self, x, mask=None): |
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| 32 | return bilinear_upsampling(x, ratio=self.ratio) |
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| 33 | def get_output_shape_for(self, input_shape): |
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| 34 | return (input_shape[0], input_shape[1], |
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| 35 | input_shape[2] * self.ratio, input_shape[3] * self.ratio) |
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| 36 | |||
| 37 | |||
| 38 | class LeNet: |
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| 39 | @staticmethod |
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| 40 | def build_Pereira(w, h, d, classes, weightsPath = None, alp = 0.333, dropout = 0.1): |
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| 41 | '''INPUT: |
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| 42 | INPUT WIDTH, HEIGHT, DEPTH, NUMBER OF OUTPUT CLASSES, PRELOADED WEIGHTS, PARAMETER FOR LEAKYReLU, DROPOUT PROBABILITY |
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| 43 | OUTPUT: |
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| 44 | TRAINED CNN ARCHITECTURE |
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| 45 | ''' |
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| 46 | K.set_image_dim_ordering('th') |
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| 47 | model = Sequential() |
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| 48 | |||
| 49 | |||
| 50 | #first set of CONV => CONV => CONV => LReLU => MAXPOOL |
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| 51 | model.add(Convolution2D(64, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (d, h, w), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 52 | model.add(LeakyReLU(alpha=alp)) |
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| 53 | model.add(Convolution2D(64, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (64, 33, 33), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 54 | model.add(LeakyReLU(alpha=alp)) |
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| 55 | model.add(Convolution2D(64, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (64, 33, 33), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 56 | model.add(LeakyReLU(alpha=alp)) |
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| 57 | model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2))) |
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| 58 | |||
| 59 | #second set of CONV => CONV => CONV => LReLU => MAXPOOL |
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| 60 | model.add(Convolution2D(128, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (64, 16, 16), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 61 | model.add(LeakyReLU(alpha=alp)) |
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| 62 | model.add(Convolution2D(128, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (128, 16, 16), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 63 | model.add(LeakyReLU(alpha=alp)) |
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| 64 | model.add(Convolution2D(128, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (128, 16, 16), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 65 | model.add(LeakyReLU(alpha = alp)) |
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| 66 | model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2))) |
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| 67 | |||
| 68 | #Fully connected layers |
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| 69 | |||
| 70 | # FC => LReLU => FC => LReLU |
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| 71 | model.add(Flatten()) |
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| 72 | model.add(Dropout(0.1)) |
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| 73 | model.add(Dense(256, kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1))) |
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| 74 | model.add(LeakyReLU(alp)) |
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| 75 | model.add(Dropout(0.1)) |
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| 76 | model.add(Dense(256, kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1))) |
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| 77 | model.add(LeakyReLU(alp)) |
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| 78 | model.add(Dropout(0.1)) |
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| 79 | |||
| 80 | # FC => SOFTMAX |
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| 81 | model.add(Dense(classes, kernel_initializer = 'glorot_normal', bias_initializer = constant(0.1))) |
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| 82 | model.add(Activation("softmax")) |
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| 83 | |||
| 84 | #if a pre-trained model is applied, load the weights |
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| 85 | if weightsPath is not None: |
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| 86 | model.load_weights(weightsPath) |
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| 87 | |||
| 88 | return model |
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| 89 | |||
| 90 | @staticmethod |
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| 91 | def build_Nikki(w, h, d, classes, l1=0.01, l2=0.01): |
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| 92 | |||
| 93 | K.set_image_dim_ordering('th') |
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| 94 | model = Sequential() |
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| 95 | model.add(Convolution2D(64, (7, 7), activation = 'relu', kernel_regularizer=l1_l2(), input_shape = (d, h, w) )) |
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| 96 | model.add(BatchNormalization(mode=0, axis=1)) |
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| 97 | model.add(MaxPooling2D(pool_size=(2,2), strides=(1,1))) |
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| 98 | model.add(Dropout(0.5)) |
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| 99 | model.add(Convolution2D(128, (5, 5), activation = 'relu', kernel_regularizer=l1_l2() )) |
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| 100 | model.add(BatchNormalization(mode=0, axis=1)) |
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| 101 | model.add(MaxPooling2D(pool_size=(2,2), strides=(1,1))) |
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| 102 | model.add(Dropout(0.5)) |
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| 103 | model.add(Convolution2D(128, (5, 5), activation = 'relu', kernel_regularizer=l1_l2() )) |
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| 104 | model.add(BatchNormalization(mode=0, axis=1)) |
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| 105 | model.add(MaxPooling2D(pool_size=(2,2), strides=(1,1))) |
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| 106 | model.add(Dropout(0.5)) |
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| 107 | model.add(Convolution2D(64, (3, 3), activation = 'relu', kernel_regularizer=l1_l2() )) |
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| 108 | model.add(Dropout(0.25)) |
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| 109 | model.add(Flatten()) |
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| 110 | model.add(Dense(5)) |
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| 111 | model.add(Activation('softmax')) |
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| 112 | sgd = SGD(lr=0.001, decay=0.01, momentum=0.9) |
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| 113 | model.compile(loss='categorical_crossentropy', optimizer=sgd) |
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| 114 | return model |
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| 115 | |||
| 116 | @staticmethod |
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| 117 | def unet(w, h, d): |
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| 118 | K.set_image_dim_ordering('th') |
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| 119 | |||
| 120 | inputs = Input((d, h, w)) |
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| 121 | conv1 = Convolution2D(32, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(inputs) |
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| 122 | conv1 = Convolution2D(32, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(conv1) |
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| 123 | pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) |
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| 124 | |||
| 125 | conv2 = Convolution2D(64, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(pool1) |
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| 126 | conv2 = Convolution2D(64, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(conv2) |
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| 127 | pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) |
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| 128 | |||
| 129 | conv3 = Convolution2D(128, kernel_size=(3, 3), activation='relu', padding='same', data_format = 'channels_first')(pool2) |
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| 130 | conv3 = Convolution2D(128, kernel_size=(3, 3), activation='relu', padding='same', data_format = 'channels_first')(conv3) |
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| 131 | pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) |
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| 132 | |||
| 133 | conv4 = Convolution2D(256, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(pool3) |
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| 134 | conv4 = Convolution2D(256, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(conv4) |
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| 135 | pool4 = MaxPooling2D(pool_size=(2, 2))(conv4) |
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| 136 | |||
| 137 | conv5 = Convolution2D(512, kernel_size=(3, 3), activation='relu', padding='same', data_format = 'channels_first')(pool4) |
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| 138 | conv5 = Convolution2D(512, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(conv5) |
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| 139 | # pool5 = MaxPooling2D(pool_size=(2, 2))(conv5) |
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| 140 | |||
| 141 | # convdeep = Convolution2D(1024, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(pool5) |
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| 142 | # convdeep = Convolution2D(1024, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(convdeep) |
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| 143 | |||
| 144 | # upmid = merge([Convolution2D(512, kernel_size = (2, 2), padding='same', data_format = 'channels_first')(UpSampling2D(size=(2, 2), data_format = 'channels_first')(convdeep)), conv5], mode='concat', concat_axis=1) |
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| 145 | # convmid = Convolution2D(512, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(upmid) |
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| 146 | # convmid = Convolution2D(512, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(convmid) |
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| 147 | |||
| 148 | up6 = merge([Convolution2D(256, kernel_size = (2, 2),activation='relu', padding='same', data_format = 'channels_first')(UpSampling2D(size=(2, 2), data_format = 'channels_first')(conv5)), conv4], mode='concat', concat_axis=1) |
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| 149 | conv6 = Convolution2D(256, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(up6) |
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| 150 | conv6 = Convolution2D(256, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(conv6) |
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| 151 | |||
| 152 | up7 = merge([Convolution2D(128, kernel_size = (2, 2),activation='relu', padding='same', data_format = 'channels_first')(UpSampling2D(size=(2, 2), data_format = 'channels_first')(conv6)), conv3], mode='concat', concat_axis=1) |
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| 153 | conv7 = Convolution2D(128, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(up7) |
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| 154 | conv7 = Convolution2D(128, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(conv7) |
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| 155 | |||
| 156 | up8 = merge([Convolution2D(64, kernel_size = (2, 2),activation='relu', padding='same', data_format = 'channels_first')(UpSampling2D(size=(2, 2), data_format = 'channels_first')(conv7)), conv2], mode='concat', concat_axis=1) |
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| 157 | conv8 = Convolution2D(64, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(up8) |
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| 158 | conv8 = Convolution2D(64, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(conv8) |
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| 159 | |||
| 160 | up9 = merge([Convolution2D(32, kernel_size = (2, 2),activation='relu', padding='same', data_format = 'channels_first')(UpSampling2D(size=(2, 2), data_format = 'channels_first')(conv8)), conv1], mode='concat', concat_axis=1) |
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| 161 | conv9 = Convolution2D(32, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(up9) |
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| 162 | conv9 = Convolution2D(32, kernel_size = (3, 3), activation='relu', padding='same', data_format = 'channels_first')(conv9) |
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| 163 | |||
| 164 | conv10 = Convolution2D(5, kernel_size=(1, 1), padding='same', data_format = 'channels_first')(conv9) |
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| 165 | flat = Reshape((5, h*w))(conv10) |
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| 166 | out = Activation('softmax')(flat) |
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| 167 | model = Model(input=inputs, output=out) |
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| 168 | model.compile(optimizer=Adam(lr=1e-5), loss='categorical_crossentropy', metrics=['accuracy']) |
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| 169 | |||
| 170 | return model |
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| 171 | |||
| 172 | |||
| 173 | #let's see how a Conv Net without any pooling layers fares, as pooling layers are known to reduce data, but stop overfitting |
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| 174 | @staticmethod |
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| 175 | def build_Pereira_mod(w, h, d, classes, weightsPath = None, alp = 0.333, dropout = 0.1): |
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| 176 | '''INPUT: |
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| 177 | INPUT WIDTH, HEIGHT, DEPTH, NUMBER OF OUTPUT CLASSES, PRELOADED WEIGHTS, PARAMETER FOR LEAKYReLU, DROPOUT PROBABILITY |
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| 178 | OUTPUT: |
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| 179 | TRAINED CNN ARCHITECTURE |
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| 180 | ''' |
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| 181 | K.set_image_dim_ordering('th') |
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| 182 | model = Sequential() |
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| 183 | |||
| 184 | |||
| 185 | #first set of CONV => CONV => CONV => LReLU => MAXPOOL |
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| 186 | model.add(Convolution2D(64, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (d, h, w), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 187 | model.add(LeakyReLU(alpha=alp)) |
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| 188 | model.add(Convolution2D(64, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (64, 33, 33), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 189 | model.add(LeakyReLU(alpha=alp)) |
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| 190 | model.add(Convolution2D(64, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (64, 33, 33), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 191 | model.add(LeakyReLU(alpha=alp)) |
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| 192 | |||
| 193 | #second set of CONV => CONV => CONV => LReLU => MAXPOOL |
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| 194 | model.add(Convolution2D(128, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (64, 16, 16), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 195 | model.add(LeakyReLU(alpha=alp)) |
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| 196 | model.add(Convolution2D(128, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (128, 16, 16), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 197 | model.add(LeakyReLU(alpha=alp)) |
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| 198 | model.add(Convolution2D(128, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (128, 16, 16), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 199 | model.add(LeakyReLU(alpha = alp)) |
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| 200 | model.add(MaxPooling2D(pool_size=(2, 2), strides=(1, 1))) |
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| 201 | |||
| 202 | #Fully connected layers |
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| 203 | |||
| 204 | # FC => LReLU => FC => LReLU |
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| 205 | model.add(Flatten()) |
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| 206 | model.add(Dropout(0.1)) |
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| 207 | model.add(Dense(128, kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1))) |
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| 208 | model.add(LeakyReLU(alp)) |
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| 209 | model.add(Dropout(0.1)) |
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| 210 | model.add(Dense(128, kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1))) |
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| 211 | model.add(LeakyReLU(alp)) |
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| 212 | model.add(Dropout(0.1)) |
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| 213 | |||
| 214 | # FC => SOFTMAX |
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| 215 | model.add(Dense(classes, kernel_initializer = 'glorot_normal', bias_initializer = constant(0.1))) |
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| 216 | model.add(Activation("softmax")) |
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| 217 | |||
| 218 | #if a pre-trained model is applied, load the weights |
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| 219 | if weightsPath is not None: |
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| 220 | model.load_weights(weightsPath) |
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| 221 | |||
| 222 | return model |
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| 223 | |||
| 224 | @staticmethod |
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| 225 | def build_Pereira_no_pooling(w, h, d, classes, weightsPath = None, alp = 0.333, dropout = 0.1): |
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| 226 | '''INPUT: |
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| 227 | INPUT WIDTH, HEIGHT, DEPTH, NUMBER OF OUTPUT CLASSES, PRELOADED WEIGHTS, PARAMETER FOR LEAKYReLU, DROPOUT PROBABILITY |
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| 228 | OUTPUT: |
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| 229 | TRAINED CNN ARCHITECTURE |
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| 230 | ''' |
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| 231 | K.set_image_dim_ordering('th') |
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| 232 | model = Sequential() |
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| 233 | |||
| 234 | |||
| 235 | #first set of CONV => CONV => CONV => LReLU => MAXPOOL |
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| 236 | model.add(Convolution2D(64, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (d, h, w), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 237 | model.add(LeakyReLU(alpha=alp)) |
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| 238 | model.add(Convolution2D(64, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (64, 33, 33), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 239 | model.add(LeakyReLU(alpha=alp)) |
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| 240 | model.add(Convolution2D(64, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (64, 33, 33), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 241 | model.add(LeakyReLU(alpha=alp)) |
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| 242 | model.add(Convolution2D(128, kernel_size=(3, 3), strides = (2, 2), padding="valid", data_format='channels_first', input_shape = (64, 33, 33), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 243 | model.add(LeakyReLU(alpha=alp)) |
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| 244 | |||
| 245 | #second set of CONV => CONV => CONV => LReLU => MAXPOOL |
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| 246 | model.add(Convolution2D(128, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (128, 16, 16), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 247 | model.add(LeakyReLU(alpha=alp)) |
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| 248 | model.add(Convolution2D(128, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (128, 16, 16), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 249 | model.add(LeakyReLU(alpha=alp)) |
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| 250 | model.add(Convolution2D(128, kernel_size=(3, 3), padding="same", data_format='channels_first', input_shape = (128, 16, 16), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 251 | model.add(LeakyReLU(alpha = alp)) |
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| 252 | model.add(Convolution2D(256, kernel_size=(3, 3), strides = (2, 2), padding="valid", data_format='channels_first', input_shape = (64, 33, 33), kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1) )) |
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| 253 | model.add(LeakyReLU(alpha=alp)) |
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| 254 | #Fully connected layers |
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| 255 | |||
| 256 | # FC => LReLU => FC => LReLU |
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| 257 | model.add(Flatten()) |
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| 258 | model.add(Dropout(0.1)) |
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| 259 | model.add(Dense(128, kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1))) |
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| 260 | model.add(LeakyReLU(alp)) |
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| 261 | model.add(Dropout(0.1)) |
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| 262 | model.add(Dense(128, kernel_initializer = 'glorot_normal', bias_initializer=constant(0.1))) |
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| 263 | model.add(LeakyReLU(alp)) |
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| 264 | model.add(Dropout(0.1)) |
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| 265 | |||
| 266 | # FC => SOFTMAX |
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| 267 | model.add(Dense(classes, kernel_initializer = 'glorot_normal', bias_initializer = constant(0.1))) |
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| 268 | model.add(Activation("softmax")) |
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| 269 | |||
| 270 | #if a pre-trained model is applied, load the weights |
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| 271 | if weightsPath is not None: |
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| 272 | model.load_weights(weightsPath) |
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| 273 | |||
| 274 | return model |