How to specify input_shape in Conv2D layer in Tensor Flow 2.0, keras

Question

I'm building an image classifier model which classifies Handwritten digits MNIST 28x28 grayscale images using CNN Here is my layer defination

    model = keras.Sequential()

model.add(keras.layers.Conv2D(64,(3,3),activation='relu',input_shape=(28,28,1)))
model.add(keras.layers.MaxPool2D((2,2)))
model.add(keras.layers.Conv2D(64,(3,3),activation='relu'))
model.add(keras.layers.MaxPool2D((2,2)))

model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(200,activation='relu'))
model.add(keras.layers.Dense(10,activation='softmax'))

But i get this error when i fit the model

 ValueError: Input 0 of layer sequential_6 is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: [32, 28, 28]

And also i want to know why we should mention 1 in input_shape in Conv2D layer.The image shape is 28x28 but we should mention 1 there.

Answer 1

The minimal change that should work is to change the line:

model.add(keras.layers.Conv2D(64,(3,3),activation='relu',input_shape=(28,28,1)))

to this, dropping the 1 :

model.add(keras.layers.Conv2D(64,(3,3),activation='relu',input_shape=(28,28)))

The reason you have the error is that your input image is 28x28 and the batch size you feed into the network has 32 images, thus an array of dimension [32, 28, 28]. Unfortunately I don't see how you feed the input to the network. But what your current code expect is an array of dimension [32, 28, 28, 1]. If that's a numpy array that you can manipulate, just reshape() it to such dimension will solve the problem.

What I suggested above is to do the other way round, ask the network to expect each image of 2D array of dimension [28,28] instead of 3D array of dimension [28,28,1]

Update:

You provided the following code change that made it work:

 train_image=train_image.reshape(60000, 28, 28, 1) 
 train_image=train_image / 255.0
 test_image = test_image.reshape(10000, 28, 28, 1) 
 test_image=test_image/255.0

What this does is that your input images are in a single huge numpy array and you fit your model with it directly. The model fit function will select "tensors" from this array from its first dimension and create a batch for each training step. The batch size is 32, so it will implicitly create an array of shape (32, 28, 28, 1) and pass it down the layers. The 2nd to 4th dimension is merely copied from the original array.

The reshape() command is to change the dimension of the array. Your original array before reshape was (60000, 28, 28) and if you lay it out as a single sequence of numbers, there will be 6000x28x28 floats. What reshape() does is to pick up these numbers and fill them into a (60000, 28, 28, 1) array, which expects 60000x28x28x1 numbers, so it can be filled exactly.