CODEWITHSUNDEEP
pythontensorflowdeep-learningkerasconv-neural-network
Lucky
Lucky

Reputation: 905

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

I have checked all the solutions, but still, I am facing the same error. My training images shape is (26721, 32, 32, 1), which I believe it is 4 dimension, but I don't know why error shows it is 5 dimension. 

 model = Sequential()

 model.add(Convolution2D(16, 5, 5, border_mode='same', input_shape= input_shape ))

So this is how I am defining model.fit_generator 

model.fit_generator(train_dataset, train_labels, nb_epoch=epochs, verbose=1,validation_data=(valid_dataset, valid_labels), nb_val_samples=valid_dataset.shape[0],callbacks=model_callbacks)

Upvotes: 65

Views: 152643

Answers (6)

Sanketsz
Sanketsz

Reputation: 136

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.

Upvotes: 2

Akash Desai
Akash Desai

Reputation: 518

I have faced 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. .. & the problem is solved

Upvotes: 0

Akash Desai
Akash Desai

Reputation: 518

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

Upvotes: 7

dgamer
dgamer

Reputation: 155

Here you need to check the "channels_first" whenever CNN is used as 2d,Also reshape your train_data and test data as:

if K.image_data_format() == 'channels_first':   #check for channels_first
 train_img.reshape(train_img.shape[0],1,x,x)
 Input_shape=(1,x,x)                            #In your case x is 32
else:
 train_img.reshape(train_img.shape[0],x,x,1)
 Input_shape=(x,x,1)

Upvotes: 0

SANDEEP KUMAR H
SANDEEP KUMAR H

Reputation: 121

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.

Upvotes: 9

Daniel Möller
Daniel Möller

Reputation: 86650

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).

Upvotes: 71

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