out = inp
for i in range(1, len(model_original.layers)): # don't include the input layer
lay = model_original.layers[i]
config = lay.get_config()
#print(config)
if type(lay) == MaxPooling2D:
#print("Pooling")
#out = AveragePooling2D(
# pool_size = lay.pool_size,
# strides = lay.strides,
# padding = lay.padding)(out)
new_lay = AveragePooling2D.from_config(config)
out = new_lay(out)
else:
new_lay = Conv2D.from_config(config)
#print(new_lay.get_config())
#print(new_lay.get_weights())
#print(new_lay.filters, new_lay.kernel_size)
out = new_lay(out)
new_lay.set_weights(lay.get_weights())
model = Model(inp, out)
model.summary()
# Define the loss
# Content loss
content_layers = ['block4_conv2']
content_loss_op = tf.Variable(0.0)
from tensorflow.python.keras.layers import Dense, Conv2D import numpy as np lay = Conv2D(filters=10, kernel_size=(3, 3)) print(lay.get_weights()) lay.set_weights(lay.get_weights()) print(lay.get_config()) new_lay = Conv2D.from_config(lay.get_config()) print(new_lay.get_weights())
