When creating a Sequential model in Keras, I understand you provide the input shape in the first layer. Does this input shape then make an implicit input layer?
For example, the model below explicitly specifies 2 Dense layers, but is this actually a model with 3 layers consisting of one input layer implied by the input shape, one hidden dense layer with 32 neurons, and then one output layer with 10 possible outputs?
model = Sequential([ Dense(32, input_shape=(784,)), Activation('relu'), Dense(10), Activation('softmax'), ])
Well, it actually is an implicit input layer indeed, i.e. your model is an example of a "good old" neural net with three layers - input, hidden, and output. This is more explicitly visible in the Keras Functional API (check the example in the docs), in which your model would be written as:
inputs = Input(shape=(784,)) # input layer x = Dense(32, activation='relu')(inputs) # hidden layer outputs = Dense(10, activation='softmax')(x) # output layer model = Model(inputs, outputs)
Actually, this implicit input layer is the reason why you have to include an input_shape argument only in the first (explicit) layer of the model in the Sequential API - in subsequent layers, the input shape is inferred from the output of the previous ones (see the comments in the source code of core.py).
You may also find the documentation on tf.contrib.keras.layers.Input enlightening.
