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

expected dense_1_input to have 3 dimensions, but got array with shape
expected input_1 to have 4 dimensions
expected dense_1_input to have 2 dimensions, but got array with shape
error when checking input: expected flatten_1_input to have 3 dimensions
error when checking input: expected input_2 to have 2 dimensions, but got array with shape
conv2d keras
valueerror: error when checking input: expected input_1 to have shape
keras input shape

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)))

using

history = model.fit_generator(get_training_data(),
                samples_per_epoch=1, nb_epoch=1,nb_val_samples=5,
                verbose=1,validation_data=get_validation_data()

with the following generator:

def get_training_data(self):
     while 1:
        for i in range(1,5):
            image = self.X_train[i]
            label = self.Y_train[i]
            yield (image,label)

(validation generator looks similar).

During training, I get the error:

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

How can that be, with a first layer

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

?


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 like so:

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

Getting ValueError: Error when checking input: expected , Hi, I am trying to train a model on some grayscale images. Getting ValueError: Error when checking input: expected conv2d_1_input to have 4 dimensions, but got array with shape (315 model.add(Convolution2D(32, 3, 3)) model.add( Activation('relu')) 562 str(len(shape)) + ' dimensions, but got array ' All you need to do is give input_shape=(32, 32, 3). Also if you use this shape then you must use tf as your image ordering. backend.set_image_dim_ordering('tf') .


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.

Error in Convolutional Neural network for input shape, Error when checking model input: expected convolution2d_input_1 to have 4 dimensions, but got array with shape (32, 32, 3)� Dismiss Join GitHub today. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.


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.

expected lstm_1_input to have 3 dimensions, but got array with shape, ValueError: Error when checking model input: expected convolution2d_input_1 to have shape (None, 3, 32, 32) but got array with shape (32, 32, 32, 3) That script is written for theano tensor shape conventions, which is� In reshaping our test images, we should be careful with input_size of the image like : model.predict(X_train.reshape(10,28,28,1)-->for 10 input images. If we want to predict for single instance of image, we should consider only one image but not number of features.


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)

Error when checking input: expected conv2d_1_input to have shape , So, while using fit function, it shows ValueError: Error when checking input: expected conv2d_1_input to have 4 dimensions, but got array with� Dismiss Join GitHub today. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.


You should simply apply the following transformation to your input data array.

input_data = input_data.reshape((-1, image_side1, image_side2, channels))

ValueError: Error when checking input: expected conv2d_1_input to , Error when checking target: expected dense_1 to have 3 dimensions, but got array with x expected dense_1 to have 4 dimensions, but got array with shape ( 32, Error when checking model input: expected convolution2d_input_1 to have 4� according to my code, I set my input shape, 15 and 512 so when I want to predict the polarity of a new sentence say:"I am so sorry" for example, with the length: 4 - I face this error: ** expected conv1d_1_input to have shape (15, 512) but got array with shape (4, 512) ** `` and this is a part of my code: model = Sequential()


Reshaping image data in Keras to match CNN requirements, Error when checking input: expected conv2d_1_input to have shape (3, 32, 32) but got array with shape (32, 32, 3). 0 votes. 1� 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).


checking input: expected conv2d_1_input to have 4 dimensions, but got array with shape 4D tensor with shape: (batch, channels, rows, cols) if data_format is 形状的数组(50000,32,32,3) - Error when checking model input: expected convolution2d_input_1 to have shape (None, 3, 32, 32) but got array� input_shape=(32, 32, 3) means the input shape should be (batch_size, 32, 32, 3). Your generator should yield batches. So if you have 300 examples, make your generator return items in shape (30,32,32,3) and it will run 10 batches of 30 items.


As the error says: expected convolution2d_input_1 to have shape (None, 3, 108, 192) # expected width = 108 and height = 192 but got array with shape (1, 3, 192, 717 model.add(Convolution2D(32, 3, 3, input_shape=(img_height, img_width, to fine-tuning, and must I use the image size which is 224*224 for this net? This issue has been automatically marked as stale because it has not had recent activity. It will be closed after 30 days if no further activity occurs, but feel free to re-open a closed issue if needed.