def res_block(X, filter, stage):
    
  # CONVOLUTIONAL BLOCK
  X_copy = X
  f1 , f2, f3 = filter

  # Main Path
  X = Conv2D(f1, (1,1), strides = (1,1), name ='res_'+str(stage)+'_conv_a', kernel_initializer= glorot_uniform(seed = 0))(X)
  X = MaxPool2D((2,2))(X)
  X = BatchNormalization(axis =3, name = 'bn_'+str(stage)+'_conv_a')(X)
  X = Activation('relu')(X) 

  X = Conv2D(f2, kernel_size = (3,3), strides =(1,1), padding = 'same', name ='res_'+str(stage)+'_conv_b', kernel_initializer= glorot_uniform(seed = 0))(X)
  X = BatchNormalization(axis =3, name = 'bn_'+str(stage)+'_conv_b')(X)
  X = Activation('relu')(X) 

  X = Conv2D(f3, kernel_size = (1,1), strides =(1,1),name ='res_'+str(stage)+'_conv_c', kernel_initializer= glorot_uniform(seed = 0))(X)
  X = BatchNormalization(axis =3, name = 'bn_'+str(stage)+'_conv_c')(X)

  # Short path
  X_copy = Conv2D(f3, kernel_size = (1,1), strides =(1,1),name ='res_'+str(stage)+'_conv_copy', kernel_initializer= glorot_uniform(seed = 0))(X_copy)
  X_copy = MaxPool2D((2,2))(X_copy)
  X_copy = BatchNormalization(axis =3, name = 'bn_'+str(stage)+'_conv_copy')(X_copy)

  # Add data from main and short paths
  X = Add()([X,X_copy])
  X = Activation('relu')(X)

    
    
  # IDENTITY BLOCK 1
  X_copy = X
    
  # Main Path
  X = Conv2D(f1, (1,1),strides = (1,1), name ='res_'+str(stage)+'_identity_1_a', kernel_initializer= glorot_uniform(seed = 0))(X)
  X = BatchNormalization(axis =3, name = 'bn_'+str(stage)+'_identity_1_a')(X)
  X = Activation('relu')(X) 

  X = Conv2D(f2, kernel_size = (3,3), strides =(1,1), padding = 'same', name ='res_'+str(stage)+'_identity_1_b', kernel_initializer= glorot_uniform(seed = 0))(X)
  X = BatchNormalization(axis =3, name = 'bn_'+str(stage)+'_identity_1_b')(X)
  X = Activation('relu')(X) 

  X = Conv2D(f3, kernel_size = (1,1), strides =(1,1),name ='res_'+str(stage)+'_identity_1_c', kernel_initializer= glorot_uniform(seed = 0))(X)
  X = BatchNormalization(axis =3, name = 'bn_'+str(stage)+'_identity_1_c')(X)

  # Add both paths together (Note that we feed the original input as is hence the name "identity")
  X = Add()([X,X_copy])
  X = Activation('relu')(X)

    
    
  # IDENTITY BLOCK 2
  X_copy = X

  # Main Path
  X = Conv2D(f1, (1,1),strides = (1,1), name ='res_'+str(stage)+'_identity_2_a', kernel_initializer= glorot_uniform(seed = 0))(X)
  X = BatchNormalization(axis =3, name = 'bn_'+str(stage)+'_identity_2_a')(X)
  X = Activation('relu')(X) 

  X = Conv2D(f2, kernel_size = (3,3), strides =(1,1), padding = 'same', name ='res_'+str(stage)+'_identity_2_b', kernel_initializer= glorot_uniform(seed = 0))(X)
  X = BatchNormalization(axis =3, name = 'bn_'+str(stage)+'_identity_2_b')(X)
  X = Activation('relu')(X) 

  X = Conv2D(f3, kernel_size = (1,1), strides =(1,1),name ='res_'+str(stage)+'_identity_2_c', kernel_initializer= glorot_uniform(seed = 0))(X)
  X = BatchNormalization(axis =3, name = 'bn_'+str(stage)+'_identity_2_c')(X)

  # Add both paths together (Note that we feed the original input as is hence the name "identity")
  X = Add()([X,X_copy])
  X = Activation('relu')(X)

  return X

input_shape = (96,96,1)

# Input tensor shape
X_input = Input(input_shape)

# Zero-padding
X = ZeroPadding2D((3,3))(X_input)

# Stage #1
X = Conv2D(64, (7,7), strides= (2,2), name = 'conv1', kernel_initializer= glorot_uniform(seed = 0))(X)
X = BatchNormalization(axis =3, name = 'bn_conv1')(X)
X = Activation('relu')(X)
X = MaxPooling2D((3,3), strides= (2,2))(X)

# Stage #2
X = res_block(X, filter= [64,64,256], stage= 2)

# Stage #3
X = res_block(X, filter= [128,128,512], stage= 3)


# Average Pooling
X = AveragePooling2D((2,2), name = 'Averagea_Pooling')(X)

# Final layer
X = Flatten()(X)
X = Dense(4096, activation = 'relu')(X)
X = Dropout(0.2)(X)
X = Dense(2048, activation = 'relu')(X)
X = Dropout(0.1)(X)
X = Dense(30, activation = 'relu')(X)


model = Model( inputs= X_input, outputs = X)
model.summary()
