Error when checking model input: expected convolution2d_input_1 to have 4 dimensions, but got array with shape (32, 32, 3)

Question

I want to train a deep network starting with the following layer:

model = Sequential()
model.add(Conv2D(32, 3, 3, input_shape=(32, 32, 3)))

Answer 1

Got the same problem, non of the answers worked for me. After a lot of debugging I found out that the size of one image was smaller than 32. This leads to a broken array with wrong dimensions and the above mentioned error.

To solve the problem, make sure that all images have the correct dimensions.

Answer 2

It is as simple as to Add one dimension, so I was going through the tutorial taught by Siraj Rawal on CNN Code Deployment tutorial, it was working on his terminal, but the same code was not working on my terminal, so I did some research about it and solved, I don't know if that works for you all. Here I have come up with solution;

Unsolved code lines which gives you problem:

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    print(x_train.shape)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols)
    input_shape = (img_rows, img_cols, 1)

Solved Code:

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    print(x_train.shape)
    input_shape = (1, img_rows, img_cols)
else:
    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, 1)

Please share the feedback here if that worked for you.

Answer 3

it depends on how you actually order your data,if its on a channel first basis then you should reshape your data: x_train=x_train.reshape(x_train.shape[0],channel,width,height)

if its channel last: x_train=s_train.reshape(x_train.shape[0],width,height,channel)

Answer 4

The input shape you have defined is the shape of a single sample. The model itself expects some array of samples as input (even if its an array of length 1).

Your output really should be 4-d, with the 1st dimension to enumerate the samples. i.e. for a single image you should return a shape of (1, 32, 32, 3).

You can find more information here under "Convolution2D"/"Input shape"

Edit: Based on Danny's comment below, if you want a batch size of 1, you can add the missing dimension using this:

image = np.expand_dims(image, axis=0)

Answer 5

I got the same error while working with mnist data set, looks like a problem with the dimensions of X_train. I added another dimension and it solved the purpose.

X_train, X_test, \ y_train, y_test = train_test_split(X_reshaped, y_labels, train_size = 0.8, random_state = 42)

X_train = X_train.reshape(-1,28, 28, 1)

X_test = X_test.reshape(-1,28, 28, 1)

Answer 6

Probably very trivial, but I solved it by just converting the input to numpy array.

For the neural network architecture,

    model = Sequential()
    model.add(Conv2D(32, (5, 5), activation="relu", input_shape=(32, 32, 3)))

When the input was,

    n_train = len(train_y_raw)
    train_X = [train_X_raw[:,:,:,i] for i in range(n_train)]
    train_y = [train_y_raw[i][0] for i in range(n_train)]

I got the error,

But when I changed it to,

   n_train = len(train_y_raw)
   train_X = np.asarray([train_X_raw[:,:,:,i] for i in range(n_train)])
   train_y = np.asarray([train_y_raw[i][0] for i in range(n_train)])

It fixed the issue.