Keras: difference of InputLayer and Input

Question

I made a model using Keras with Tensorflow. I use Inputlayer with these lines of code:

img1 = tf.placeholder(tf.float32, shape=(None, img_width, img_heigh, img_ch))
first_input = InputLayer(input_tensor=img1, input_shape=(img_width, img_heigh, img_ch)) 
first_dense = Conv2D(16, 3, 3, activation='relu', border_mode='same', name='1st_conv1')(first_input)

But I get this error:

ValueError: Layer 1st_conv1 was called with an input that isn't a symbolic tensor. Received type: <class 'keras.engine.topology.InputLayer'>. Full input: [<keras.engine.topology.InputLayer object at 0x00000000112170F0>]. All inputs to the layer should be tensors.

When I use Input like this, it works fine:

first_input = Input(tensor=img1, shape=(224, 224, 3), name='1st_input')
first_dense = Conv2D(16, 3, 3, activation='relu', border_mode='same', name='1st_conv1')(first_input)

What is the difference between Inputlayer and Input?

Answer 1

• InputLayer is a layer.
• Input is a tensor.

You can only call layers passing tensors to them.

The idea is:

outputTensor = SomeLayer(inputTensor)

So, only Input can be passed because it's a tensor.

Honestly, I have no idea about the reason for the existence of InputLayer. Maybe it's supposed to be used internally. I never used it, and it seems I'll never need it.

Answer 2

According to tensorflow website, "It is generally recommend to use the functional layer API via Input, (which creates an InputLayer) without directly using InputLayer." Know more at this page here

Answer 3

Input: Used for creating a functional model

inp=tf.keras.Input(shape=[?,?,?])
x=layers.Conv2D(.....)(inp)

Input Layer: used for creating a sequential model

x=tf.keras.Sequential()
x.add(tf.keras.layers.InputLayer(shape=[?,?,?]))

And the other difference is that

When using InputLayer with the Keras Sequential model, it can be skipped by moving the input_shape parameter to the first layer after the InputLayer.

That is in sequential model you can skip the InputLayer and specify the shape directly in the first layer. i.e From this

model = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=(4,)),
tf.keras.layers.Dense(8)])

To this

model = tf.keras.Sequential([
tf.keras.layers.Dense(8, input_shape=(4,))])

Answer 4

To define it in simple words:

keras.layers.Input is used to instantiate a Keras Tensor. In this case, your data is probably not a tf tensor, maybe an np array.

On the other hand, keras.layers.InputLayer is a layer where your data is already defined as one of the tf tensor types, i.e., can be a ragged tensor or constant or other types.

I hope this helps!