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

[Ralph Fehrer]

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)))

?

[Benjamin Striner]

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.

Cheers