GradientTape: getting gradient of nan

user_17523218 0
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I'm trying to calculate the gradient within tensorflow, however returning None. I have already adjusted the type to be tensorflow.python.framework.ops.EagerTensor, however htis did not solve the problem.

This is the code so far:

accuracy = tf.keras.metrics.CategoricalAccuracy('accuracy')
loss = tf.keras.metrics.CategoricalCrossentropy('loss')
  
for epoch in range(epochs):
    accuracy.reset_states()
    loss.reset_states()
    
    for batch in iterate_minibatches(X_train, y_train, batch_size):
        imgs = batch[0]
        labels = batch[1]
        with tf.GradientTape() as tape:
            preds = model(imgs)
            
            labels = tf.convert_to_tensor(labels, dtype=tf.float32)
            
            #print(loss(labels,preds))
            # Loss is crossentropy loss with regularization term for each parameter
            total_loss = loss(labels, preds) #+l2_penalty(model, theta_A) 

        grads = tape.gradient(total_loss, model.trainable_variables)
        model.optimizer.apply_gradients(zip(grads, model.trainable_variables))
       
        accuracy.update_state(labels, preds)
        loss.update_state(labels, preds)
        print("\rEpoch: {}, Batch: {}, Loss: {:.3f}, Accuracy: {:.3f}".format(
            epoch+1, batch+1, loss.result().numpy(), accuracy.result().numpy()), flush=True, end='')
        print("")
   
print("Task B accuracy after training trained model on Task B: {}".format(model.evaluate(task_B_test)))
print("Task A accuracy after training trained model on Task B: {}".format(model.evaluate(task_A_test)))

does anybody know why it's turning none or how i can fix this?

Edit: My error message looks the following:

AttributeError Traceback (most recent call last) C:\Users\DC5DE~1.ALB\AppData\Local\Temp/ipykernel_13300/818221091.py in 34 grads = tape.gradient(total_loss, model.trainable_variables) 35 ---> 36 model.optimizer.apply_gradients(zip(grads, model.trainable_variables)) 37 38 accuracy.update_state(labels, preds)

AttributeError: 'NoneType' object has no attribute 'apply_gradients'

Since I am not sure if it has to do with how I pass my image data to GradientTape here is my function for the minibatch:

def iterate_minibatches(inputs, targets, batchsize, shuffle=False):
    assert inputs.shape[0] == targets.shape[0]
    if shuffle:
        indices = np.arange(inputs.shape[0])
        np.random.shuffle(indices)
    for start_idx in range(0, inputs.shape[0] - batchsize + 1, batchsize):
        if shuffle:
            excerpt = indices[start_idx:start_idx + batchsize]
        else:
            excerpt = slice(start_idx, start_idx + batchsize)
        yield inputs[excerpt], targets[excerpt]

Also: a similar problem was mentioned here, however without any solution that works.

keras tensorflow python
2 Answers

You need to use tf.keras.losses.CategoricalCrossentropy for loss computation instead of tf.keras.metrics.CategoricalCrossentropy which works differently and will stop gradient propagation.

user_55239200

You are getting a few things mixed up. You either need to call model.compile or define your own optimizer and use it. Also, you should not mix up your metrics with your loss function. Here is a working example:

import tensorflow as tf

accuracy = tf.keras.metrics.CategoricalAccuracy('accuracy')
metric = tf.keras.metrics.CategoricalCrossentropy('metric_ categorical_crossentropy')
loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
epochs = 2
model = tf.keras.Sequential([
    tf.keras.layers.Dense(units=3, input_shape=(1,))
]) 
optimizer = tf.keras.optimizers.Adam()

dataset = tf.data.Dataset.from_tensor_slices((tf.random.normal((50, 1)), tf.random.normal((50, 3)))).batch(5)
for epoch in range(epochs):
    accuracy.reset_states()
    metric.reset_states()
    
    for i, batch in enumerate(dataset):
        imgs = batch[0]
        labels = batch[1]
        print(imgs.shape, labels.shape)
        with tf.GradientTape() as tape:
            preds = model(imgs)
                      
            #print(loss(labels,preds))
            # Loss is crossentropy loss with regularization term for each parameter
            total_loss = loss(labels, preds) #+l2_penalty(model, theta_A) 

        grads = tape.gradient(total_loss, model.trainable_variables)
        optimizer.apply_gradients(zip(grads, model.trainable_variables))
       
        accuracy.update_state(labels, preds)
        metric.update_state(labels, preds)
        print("\rEpoch: {}, Batch: {}, Loss: {:.3f}, Accuracy: {:.3f}".format(
            epoch+1, i+1, metric.result().numpy(), accuracy.result().numpy()), flush=True, end='')
        print("")

Epoch: 1, Batch: 1, Loss: 4.209, Accuracy: 0.200
Epoch: 1, Batch: 2, Loss: 1.641, Accuracy: 0.400
Epoch: 1, Batch: 3, Loss: 1.294, Accuracy: 0.333
Epoch: 1, Batch: 4, Loss: 1.025, Accuracy: 0.300
Epoch: 1, Batch: 5, Loss: -0.110, Accuracy: 0.320
Epoch: 1, Batch: 6, Loss: 0.316, Accuracy: 0.267
Epoch: 1, Batch: 7, Loss: -0.118, Accuracy: 0.257
Epoch: 1, Batch: 8, Loss: -0.284, Accuracy: 0.225
Epoch: 1, Batch: 9, Loss: -0.249, Accuracy: 0.244
Epoch: 1, Batch: 10, Loss: -0.464, Accuracy: 0.260
Epoch: 2, Batch: 1, Loss: 4.468, Accuracy: 0.200
Epoch: 2, Batch: 2, Loss: 1.578, Accuracy: 0.400
Epoch: 2, Batch: 3, Loss: 1.012, Accuracy: 0.400
Epoch: 2, Batch: 4, Loss: 0.836, Accuracy: 0.350
Epoch: 2, Batch: 5, Loss: -0.294, Accuracy: 0.360
Epoch: 2, Batch: 6, Loss: 0.168, Accuracy: 0.300
Epoch: 2, Batch: 7, Loss: -0.201, Accuracy: 0.286
Epoch: 2, Batch: 8, Loss: -0.634, Accuracy: 0.250
Epoch: 2, Batch: 9, Loss: -0.552, Accuracy: 0.267
Epoch: 2, Batch: 10, Loss: -0.920, Accuracy: 0.280
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