CODEWITHSUNDEEP
pythontensorflowkerasconv-neural-network
nerdsley
nerdsley

Reputation: 223

TensorFlow Keras dimension error for input layer

I've searched through all the solutions related to this, and I still can't figure out how to shape my training data so Tensorflow accepts it.

My training data is a numpy array of shape (21005, 48, 48), where the 21005 is number of elements and the 48,48 is a 48x48 grayscale image.

model.add(tf.keras.layers.Conv2D(64, kernel_size=3,activation='relu',input_shape=(48,48,1)))
model.add(tf.keras.layers.Conv2D(32, kernel_size=3,activation='relu'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(7, activation='softmax'))

model.compile(optimizer='adam',
              loss = 'sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(image_train, emotion_train,batch_size=BATCH_SIZE,epochs=EPOCHS, verbose=1)

When I run the fit function, however, it returns an error stating:

ValueError: Error when checking input: expected conv2d_input to have 4 dimensions, but got array with shape (21005, 48, 48)

This leads me to think I'm formatting the input data incorrectly, or missing something regarding how Keras and TF actually pass the input image into the input layer. I've tried adding the extra dimension to the input shape to allow for a channel in a 2d Conv layer, as well as reshaping the images themselves to no avail. Any advice?

Upvotes: 2

Views: 1835

Answers (2)

Timbus Calin
Timbus Calin

Reputation: 14993

When you made your preprocessing, you might have read the image in grayscale mode with a library OpenCV/PIL.

When you read them, your library considers a grayscale image of size (48,48), not a (48,48,1), hence the issue that you have.

Solve the issue as soon as possible, not before feeding to your model; in your code, wherever you read those images, before appending to your list/arrays, ensure the right shape of the array is picked. You can see down below an OpenCV example:

image = cv2.imread(filepath, 0)
#Before this np_expand_dims, image has shape (48,48)
image = np.expand_dims(image , axis=2)
#After this step, image has shape (48,48,1)

Upvotes: 1

Balraj Ashwath
Balraj Ashwath

Reputation: 1427

Reshape your training data to have 4-dimensions before calling model.fit() such as:

image_train = np.reshape(image_train, (21005, 48, 48, 1))

This is needed because the first Conv2D layer expects an image to have an input_shape of (48,48,1)

Upvotes: 2

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