ValueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: [8, 28, 28]

I keep on getting this error related to input shape. Any help would be highly appreciated. Thanks!

import tensorflow as tf

(xtrain, ytrain), (xtest, ytest) = tf.keras.datasets.mnist.load_data()

model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(16, kernel_size=3, activation='relu'),
    tf.keras.layers.MaxPooling2D(pool_size=2),
    tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu'),
    tf.keras.layers.MaxPooling2D(pool_size=2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
    ])

model.compile(loss='categorical_crossentropy', 
              optimizer='adam',
              metrics='accuracy')

history = model.fit(xtrain, ytrain,
                    validation_data=(xtest, ytest),
                    epochs=10, batch_size=8)

ValueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: [8, 28, 28]

The input layers of the model you created needs a 4 dimension tensor to work with but the x_train tensor you are passing to it has only 3 dimensions

This means that you have to reshape your training set with .reshape(n_images, 286, 384, 1). Now you have added an extra dimension without changing the data and your model is ready to run.

you need to reshape your x_train tensor to a 4 dimension before training your model. for example:

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

for more info on keras inputs Check this answer

You need to add a channel dimension. Keras expects this data format:

(n_samples, height, width, channels)

For instance this, if your images are greyscale, they have 1 channel, and so they need to be given to Keras in this format:

(60000, 28, 28, 1)

Unfortunately, grayscale pictures will often be given/downloaded without a channel dimension, for instance in tf.keras.datasets.mnist.load_data, which will be (60000, 28, 28), which is problematic.

Solution:

You can use tf.expand_dims to add a dimension

xtrain = tf.expand_dims(xtrain, axis=-1)

Now your input shape will be:

(60000, 28, 28, 1)

There are other alternatives that do the same:

xtrain = xtrain[..., np.newaxis]
xtrain = xtrain[..., None]
xtrain = xtrain.reshape(-1, 28, 28, 1)
xtrain = tf.reshape(xtrain, (-1, 28, 28, 1))
xtrain = np.expand_dims(xtrain, axis=-1)