ValueError: No gradients provided for any variable: (['embedding_1/embeddings:0', 'image_layer_1/kernel:0', 'image_layer_1/bias:0',

TensorFlow General Discussion
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This is a program that generates captions for images and then updates the weights of the model based on the cross entropy loss. While updating the weights of the model, I come across this error and cannot figure out the root cause. Please guide me to fix this.

import tensorflow as tf
from tensorflow.keras.optimizers import Adam

def custom_loss(y_true, y_pred, feedback):
    # Compute cross-entropy loss
    cross_entropy_loss = tf.keras.losses.categorical_crossentropy(y_true, y_pred)
    # Compute negative feedback loss
    negative_feedback_loss = tf.reduce_mean(-1 * np.exp(feedback))
    # Combine both losses with a weighted sum
    total_loss = cross_entropy_loss + negative_feedback_loss
    return total_loss

# Define loss function and optimizer
loss_fn = custom_loss
optimizer = Adam(learning_rate=0.001)

# Update model parameters using RL algorithm
for img in os.listdir(dataset_images): # Number of RL iterations
    RL_learning_loop += 1
    if RL_learning_loop < 10:
        image_path = dataset_images + "/" + img
        image = Image.open(image_path)
        #image = image.resize(300,300)
        image.show()
        photo = extract_features(image_path, xception_model)
        in_text = 'start'
        for i in range(max_length):
            sequence = tokenizer.texts_to_sequences([in_text])[0]
            sequence = pad_sequences([sequence], maxlen=max_length)
            #preds = model.predict([photo,sequence], verbose=0)
            preds = model([photo,sequence])
            next_index = sample_pred(preds, temperature=0.5) # introduce randomness with temperature=0.5
            next_word = word_for_id(next_index, tokenizer)
            if next_word is None:
                break
            in_text += ' ' + next_word
            if next_word == 'end':
                break

        print("Generated caption:", in_text)
        
        feedback = float(input("Rate the quality of the caption (0-1): "))

        # Compute loss between predicted and ground-truth caption
        true_caption = get_true_caption(image_path, cln_descriptions) # function to get ground-truth caption
        true_sequence = tokenizer.texts_to_sequences([true_caption])[0]
        true_sequence = pad_sequences([true_sequence], maxlen=max_length)
        y_true = np.zeros((1, max_length, len(tokenizer.word_index) + 1))
        for i, word_index in enumerate(true_sequence[0]):
            y_true[0, i, word_index] = 1
        y_pred = np.zeros((1, max_length, len(tokenizer.word_index) + 1))
        for i, word_index in enumerate(sequence[0]):
            y_pred[0, i, word_index] = 1
        loss = loss_fn(y_true, y_pred, feedback)
        loss = tf.reduce_mean(loss)

        print("Loss: ",loss)

        trainable_vars = []
        for var in model.trainable_variables:
            if 'embedding' not in var.name and 'conv2d' not in var.name:
                trainable_vars.append(var)
                
        grads = tape.gradient(loss, trainable_vars)
        print("Gradient",grads)
        print("**************************")
        optimizer.apply_gradients(zip(grads, trainable_vars))

        # Update Q-table based on feedback
        for i in range(len(in_text.split())):
            word = in_text.split()[i]
            action = tokenizer.word_index[word]
            if i == len(in_text.split())-1:
                next_max_q_value = 0
            else:
                next_word = in_text.split()[i+1]
                next_action = tokenizer.word_index[next_word]
                next_max_q_value = np.max(q_table[i+1][next_action])
            q_table[i][action] += alpha * (feedback + gamma * next_max_q_value - q_table[i][action])

        # Save updated model and Q-table
        model.save('models/model_rl.h5')
        dump(q_table, open("q_table.p", "wb"))

The gradient is being calculated as

Gradient [None, None, None, None, None, None, None, None, None, None, None, None, None]

and I get the following error.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-114-6d5266f12187> in <cell line: 18>()
     64         print("Gradient",grads)
     65         print("**************************")
---> 66         optimizer.apply_gradients(zip(grads, trainable_vars))
     67 
     68         # Update model weights based on loss

3 frames
/usr/local/lib/python3.9/dist-packages/keras/optimizers/utils.py in filter_empty_gradients(grads_and_vars)
     75     if not filtered:
     76         variable = ([v.name for _, v in grads_and_vars],)
---> 77         raise ValueError(
     78             f"No gradients provided for any variable: {variable}. "
     79             f"Provided `grads_and_vars` is {grads_and_vars}."

ValueError: No gradients provided for any variable: (['image_layer_1/kernel:0', 'image_layer_1/bias:0', 'LSTM_layer_1/lstm_cell_8/kernel:0', 'LSTM_layer_1/lstm_cell_8/recurrent_kernel:0', 'LSTM_layer_1/lstm_cell_8/bias:0', 'image_layer_2/kernel:0', 'image_layer_2/bias:0', 'LSTM_layer_2/kernel:0', 'LSTM_layer_2/bias:0', 'dense/kernel:0', 'dense/bias:0', 'dense_1/kernel:0', 'dense_1/bias:0'],). Provided `grads_and_vars` is ((None, <tf.Variable 'image_layer_1/kernel:0' shape=(2048, 256) dtype=float32, numpy=
array([[-0.03491995, -0.04523179, -0.00278588, ...,  0.02073344,
3

Hi @adarsh_nl,

Sorry for the delay in response.
I could see that the gradients are being calculated as None because they are not properly tracked.I suggest to wrap the loss calculation in tf.GradientTape() for tracking gradients and ensure that all operations in your custom loss function use TensorFlow functions and convert feedback to a tensor with tf.convert_to_tensor(). In addition, use the model directly (e.g., preds = model([photo, sequence])) instead of .predict() to maintain gradients.

Here’s a sample implementation gist of using gradient tape for your reference and kindly check out this documentation for more information.

Hope this helps.Thank You.

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