TensorFlow rises issues scaling fro 2 to 3 dimensions-CodePudding

Im experiencing some problems when scaling to 3d array in TensorFlow. In a nutshell I resumed the issues in the following code.

import numpy as np
import tensorflow as tf


mymodel2d = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])
mymodel2d.compile(optimizer='adam', loss='mse')


mymodel3d = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(28, 28, 3)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])
mymodel3d.compile(optimizer='adam', loss='mse')

xx2d = np.zeros((28,28))
xx3d = np.zeros((28,28,3))

print(xx2d.shape)
print(xx3d.shape)

out1 = mymodel2d.predict(xx2d)
out2 = mymodel3d.predict(xx3d)

The model2d works properly, but on the model3d rise the following issues when trying to execute out2 = mymodel3d.predict(xx3d)line.

The rised error is: ValueError: Input 0 of layer "sequential_1" is incompatible with the layer: expected shape=(None, 28, 28, 3), found shape=(None, 28, 3)

Can someone give me a hint in understanding such a behaviour?

CodePudding user response：

Your arrays shape should begin with one, so instead call

xx2d = np.zeros((1, 28, 28))
xx3d = np.zeros((1, 28, 28, 3))

For an explanation you can look here.