Option 2: Save/Load the Entire Model from keras.models import load_model # Creates a HDF5 file 'my_model.h5' model.save('my_model.h5') # Deletes the existing model del model # Returns a compiled model identical to the previous one model = load_model('my_model.h5') This single HDF5 file will contain:

Model class API. In the functional API, given some input tensor(s) and output tensor(s), you can instantiate a Model via: from keras.models import Model from keras.layers import Input, Dense a = Input(shape=(32,)) b = Dense(32)(a) model = Model(inputs=a, outputs=b) This model will include all layers required in the computation of b given a. model = Model(inputs=inputs, outputs=outputs) return model def resnet_v2(input_shape, depth, num_classes=10): """ResNet Version 2 Model builder [b] Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or also known as bottleneck layer First shortcut connection per layer is 1 x 1 Conv2D.

Model class API. In the functional API, given some input tensor(s) and output tensor(s), you can instantiate a Model via: from keras.models import Model from keras.layers import Input, Dense a = Input(shape=(32,)) b = Dense(32)(a) model = Model(inputs=a, outputs=b) This model will include all layers required in the computation of b given a. model = Model(inputs=inputs, outputs=outputs) return model def resnet_v2(input_shape, depth, num_classes=10): """ResNet Version 2 Model builder [b] Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or also known as bottleneck layer First shortcut connection per layer is 1 x 1 Conv2D.