--- a
+++ b/DigiPathAI/models/densenet.py
@@ -0,0 +1,165 @@
+"""
+	github cite: 
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from datetime import datetime
+import os
+import glob
+import random
+
+import imgaug
+from imgaug import augmenters as iaa
+from PIL import Image
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+
+
+import numpy as np 
+import tensorflow as tf
+from tensorflow.keras import backend as K
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import (Input, BatchNormalization, Conv2D, MaxPooling2D,                             						AveragePooling2D, ZeroPadding2D, concatenate, 	
+					Concatenate, UpSampling2D, Activation, Lambda)
+from tensorflow.keras.losses import categorical_crossentropy
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard
+from tensorflow.keras import metrics
+
+
+# Densenet Model
+bn_axis = 3
+channel_axis = bn_axis
+
+def conv_block(prev, num_filters, kernel=(3, 3), strides=(1, 1), act='relu', prefix=None):
+    name = None
+    if prefix is not None:
+        name = prefix + '_conv'
+    conv = Conv2D(num_filters, kernel, padding='same', kernel_initializer='he_normal', strides=strides, name=name)(prev)
+    if prefix is not None:
+        name = prefix + '_norm'
+    conv = BatchNormalization(name=name, axis=bn_axis)(conv)
+    if prefix is not None:
+        name = prefix + '_act'
+    conv = Activation(act, name=name)(conv)
+    return conv
+
+def dense_conv_block(x, growth_rate, name):
+    """A building block for a dense block.
+    # Arguments
+        x: input tensor.
+        growth_rate: float, growth rate at dense layers.
+        name: string, block label.
+    # Returns
+        Output tensor for the block.
+    """
+    bn_axis = 3
+    x1 = BatchNormalization(axis=bn_axis,
+                                   epsilon=1.001e-5,
+                                   name=name + '_0_bn')(x)
+    x1 = Activation('relu', name=name + '_0_relu')(x1)
+    x1 = Conv2D(4 * growth_rate, 1,
+                       use_bias=False,
+                       name=name + '_1_conv')(x1)
+    x1 = BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
+                                   name=name + '_1_bn')(x1)
+    x1 = Activation('relu', name=name + '_1_relu')(x1)
+    x1 = Conv2D(growth_rate, 3,
+                       padding='same',
+                       use_bias=False,
+                       name=name + '_2_conv')(x1)
+    x = Concatenate(axis=bn_axis, name=name + '_concat')([x, x1])
+    return x
+
+def dense_block(x, blocks, name):
+    """A dense block.
+    # Arguments
+        x: input tensor.
+        blocks: integer, the number of building blocks.
+        name: string, block label.
+    # Returns
+        output tensor for the block.
+    """
+    for i in range(blocks):
+        x = dense_conv_block(x, 32, name=name + '_block' + str(i + 1))
+    return x
+
+
+def transition_block(x, reduction, name):
+    """A transition block.
+    # Arguments
+        x: input tensor.
+        reduction: float, compression rate at transition layers.
+        name: string, block label.
+    # Returns
+        output tensor for the block.
+    """
+    bn_axis = 3
+    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
+                                  name=name + '_bn')(x)
+    x = Activation('relu', name=name + '_relu')(x)
+    x = Conv2D(int(K.int_shape(x)[bn_axis] * reduction), 1,
+                      use_bias=False,
+                      name=name + '_conv')(x)
+    x = AveragePooling2D(2, strides=2, name=name + '_pool')(x)
+    return x
+
+def unet_densenet121(input_shape, weights='imagenet'):
+    blocks = [6, 12, 24, 16]
+    n_channel = 3
+    n_class = 2
+    img_input = Input(input_shape + (n_channel,))
+    
+    x = ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
+    x = Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
+    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
+                           name='conv1/bn')(x)
+    x = Activation('relu', name='conv1/relu')(x)
+    conv1 = x
+    x = ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
+    x = MaxPooling2D(3, strides=2, name='pool1')(x)
+    x = dense_block(x, blocks[0], name='conv2')
+    conv2 = x
+    x = transition_block(x, 0.5, name='pool2')
+    x = dense_block(x, blocks[1], name='conv3')
+    conv3 = x
+    x = transition_block(x, 0.5, name='pool3')
+    x = dense_block(x, blocks[2], name='conv4')
+    conv4 = x
+    x = transition_block(x, 0.5, name='pool4')
+    x = dense_block(x, blocks[3], name='conv5')
+    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
+                           name='bn')(x)
+    conv5 = x 
+    
+    conv6 = conv_block(UpSampling2D()(conv5), 320)
+    conv6 = concatenate([conv6, conv4], axis=-1)
+    conv6 = conv_block(conv6, 320)
+
+    conv7 = conv_block(UpSampling2D()(conv6), 256)
+    conv7 = concatenate([conv7, conv3], axis=-1)
+    conv7 = conv_block(conv7, 256)
+
+    conv8 = conv_block(UpSampling2D()(conv7), 128)
+    conv8 = concatenate([conv8, conv2], axis=-1)
+    conv8 = conv_block(conv8, 128)
+
+    conv9 = conv_block(UpSampling2D()(conv8), 96)
+    conv9 = concatenate([conv9, conv1], axis=-1)
+    conv9 = conv_block(conv9, 96)
+
+    conv10 = conv_block(UpSampling2D()(conv9), 64)
+    conv10 = conv_block(conv10, 64)
+    res = Conv2D(n_class, (1, 1), activation='softmax')(conv10)
+    model = Model(img_input, res)
+
+    return model
+#model = unet_densenet121(input_shape=(256,256), weights=None)
+#model.summary()
+
+
+# In[6]:
+