Python - keras

import keras as ks
import numpy as np

features = np.zeros((1,13,22))
print('features:', features.shape)

input_size = features.shape[1:]   #input_size (13, 22)
print('input_size:', input_size)

# input layer
input_layer = ks.layers.Input(shape=(*input_size,), name='input')
print('input:', input_layer.shape) # (?, 13, 22)

# noise
x = ks.layers.GaussianNoise(stddev=0.1)(input_layer)
print('1', x.shape) # (?, 13, 22)

# conv layer
x = ks.layers.Conv1D(filters=8, kernel_size=3, strides=1, activation=ks.activations.relu, padding='same')(x)
print('2', x.shape) # (?, 13, 8)

x = ks.layers.ZeroPadding1D(padding=(1, 0))(x)
print('3', x.shape) # (?, 14, 8)

x = ks.layers.MaxPool1D(pool_size=2, strides=None)(x)
print('4', x.shape) # (?, 7, 8)

x = ks.layers.Conv1D(filters=8, kernel_size=3, strides=1, activation=ks.activations.relu, padding='same')(x)
print('5', x.shape) # (?, 7, 8)

x = ks.layers.UpSampling1D(size=2)(x)
print('6', x.shape) # (?, 14, 8)

x = ks.layers.Conv1D(filters=8, kernel_size=3, strides=1, activation=ks.activations.relu, padding='same')(x)
print('7', x.shape) # (?, 14, 8)

x = ks.layers.Conv1D(filters=input_size[-1], kernel_size=3, strides=1, activation=ks.activations.relu, padding='same')(x)
print('8', x.shape) # (?, 14, 22)

# output
output_layer = x
print('output', output_layer.shape)

model = ks.models.Model(inputs=input_layer, outputs=output_layer)
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

print(model.summary())

labels = np.zeros((1,13,22)) # gives error
#labels = np.zeros((1,14,22)) # works correctly
model.fit(features, labels)  # <--- # ValueError: Error when checking target: expected conv1d_4 to have shape (14, 22) but got array with shape (13, 22)