Keras ValueError: Input 0 is incompatible with layer conv2d_1: expected ndim=4, found ndim=5

Question

The problem is input_shape.

It should actually contain 3 dimensions only. And internally keras will add the batch dimension making it 4.

Since you probably used input_shape with 4 dimensions (batch included), keras is adding the 5th.

You should use input_shape=(32,32,1).

The problem is with input_shape. Try adding an extra dimension/channel for letting keras know that you are working on a grayscale image ie -->1

input_shape= (56,56,1). Probably if you are using a normal Deep learning model then it won't raise an issue but for Convnet it does.

For reshape the data we need to add fourth dimensions i.e changing from (6000,28,28) to (6000,28,28,1)

My code is:

img_rows=x_train[0].shape[0]
img_cols=x_test[0].shape[1]

X_train=x_train.reshape(x_train.shape[0],img_rows,img_cols,1)

X_test=x_test.reshape(x_test.shape[0],img_rows,img_cols,1)


Input_shape=(img_rows,img_cols,**).  *->  I forgot to put 1 here.

I have face the same problem

Input 0 is incompatible with layer conv2d_4 : except ndim=4 ,found ndim=3

I solved this problem by simply putting value in the input shape

Input_shape=(img_rows,img_cols,1)#store the shape of single image.

With this problem is solved

you can use :

train_dataset= train_dataset.reshape(-1,32,32,1)

and now you can use input_shape(32,32,1) in the algorithm.