Tensorflow keras, no gradients error when use MSE

Question

I am trying the code from tensorflow "Writing a training loop from scratch" with some changes by myself. I changed the loss function from SparseCategoricalCrossentropy to MeanSquaredError. I also changed the architecture of the model by adding a new Lambda layer for loss calculation. However, I have the Value error that no gradients provided for variable. Is there any way that I can make the code to run with MSE?

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

inputs = keras.Input(shape=(784,), name="digits")
x1 = layers.Dense(64, activation="relu")(inputs)
x2 = layers.Dense(64, activation="relu")(x1)
outputs = layers.Dense(10, name="predictions")(x2)
final_outputs = layers.Lambda(lambda x: tf.math.argmax(x, axis = -1))(outputs)
model = keras.Model(inputs=inputs, outputs=final_outputs)

# Instantiate an optimizer.
optimizer = keras.optimizers.SGD(learning_rate=1e-3)
# Instantiate a loss function.
loss_fn = keras.losses.MeanSquaredError()

# Prepare the training dataset.
batch_size = 64
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = np.reshape(x_train, (-1, 784))
x_test = np.reshape(x_test, (-1, 784))

# Reserve 10,000 samples for validation.
x_val = x_train[-10000:]
y_val = y_train[-10000:]
x_train = x_train[:-10000]
y_train = y_train[:-10000]

# Prepare the training dataset.
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)

# Prepare the validation dataset.
val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val))
val_dataset = val_dataset.batch(batch_size)


epochs = 2
for epoch in range(epochs):
    print("\nStart of epoch %d" % (epoch,))

    for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
        with tf.GradientTape() as tape:
            logits = model(x_batch_train, training=True)

            loss_value = loss_fn(y_batch_train, logits)

        grads = tape.gradient(loss_value, model.trainable_weights)

        optimizer.apply_gradients(zip(grads, model.trainable_weights))

Answer 1

argmax ops is not differentiable. To use an integer label and MSE loss, you want to convert your labels y_train and y_val to integers.

y_train = np.argmax(y_train, axis=-1)
y_val = np.argmax(y_val, axis=-1)

And adjust the output layer to output integer labels

outputs = layers.Dense(1, name="predictions")(x2)