resnet_test.py

filters = [32,64,128] input_img = Input(shape = (61,75,1))

def block(filters, inp): 
  inp = inp layer_1 = BatchNormalization()(inp) 
  act_1 = Activation('relu')(layer_1) 
  conv_1 = Conv2D(filters, (3,3), padding = 'same')(act_1) 
  layer_2 = BatchNormalization()(conv_1) 
  act_2 = Activation('relu')(layer_2) 
  conv_2 = Conv2D(filters, (3,3), padding = 'same')(act_2) 
  return(conv_2) 

x = Conv2D(filters[0], (3,3), padding = 'same')(input_img) 
y = MaxPooling2D(padding = 'same')(x) 
x = Add()([block(filters[0], y),y]) 
y = Add()([block(filters[0], x),x]) 
x = Add()([block(filters[0], y),y]) 
x = Conv2D(filters[1], (3,3), strides = (2,2), padding = 'same', 
           activation = 'relu')(x) 
y = Add()([block(filters[1], x),x]) 
x = Add()([block(filters[1], y),y]) 
y = Add()([block(filters[1], x),x]) 
y = Conv2D(filters[2], (3,3), strides = (2,2), padding = 'same', 
           activation = 'relu')(y) 
x = Add()([block(filters[2], y),y]) 
y = Add()([block(filters[2], x),x]) 
x = Add()([block(filters[2], y),y]) 

x2 = GlobalAveragePooling2D()(x) 
x2 = Dense(len(classes), activation = 'softmax')(x2)