问题描述
我有一个简单的神经网络，正试图通过使用如下所示的回调，使用tensorboard绘制渐变：
class GradientCallback(tf.keras.callbacks.Callback):
console = False
count = 0
run_count = 0
def on_epoch_end(self,epoch,logs=None):
weights = [w for w in self.model.trainable_weights if 'dense' in w.name and 'bias' in w.name]
self.run_count += 1
run_dir = logdir+"/gradients/run-" + str(self.run_count)
with tf.summary.create_file_writer(run_dir).as_default(),tf.GradientTape() as g:
# use test data to calculate the gradients
_x_batch = test_images_scaled_reshaped[:100]
_y_batch = test_labels_enc[:100]
g.watch(_x_batch)
_y_pred = self.model(_x_batch) # forward-propagation
per_sample_losses = tf.keras.losses.categorical_crossentropy(_y_batch,_y_pred)
average_loss = tf.reduce_mean(per_sample_losses) # Compute the loss value
gradients = g.gradient(average_loss,self.model.weights) # Compute the gradient
for t in gradients:
tf.summary.histogram(str(self.count),data=t)
self.count+=1
if self.console:
print('Tensor: {}'.format(t.name))
print('{}\n'.format(K.get_value(t)[:10]))
# Set up logging
!rm -rf ./logs/ # clear old logs
from datetime import datetime
import os
root_logdir = "logs"
run_id = datetime.now().strftime("%Y%m%d-%H%M%S")
logdir = os.path.join(root_logdir,run_id)
# register callbacks,this will be used for tensor board latter
callbacks = [
tf.keras.callbacks.TensorBoard( log_dir=logdir,histogram_freq=1,write_images=True,write_grads = True ),GradientCallback()
]
然后，我在健身过程中使用以下回调：
network.fit(train_pipe,epochs = epochs,batch_size = batch_size,validation_data = val_pipe,callbacks=callbacks)
现在，当我检查张量板时，我可以在左侧滤镜上看到渐变，但是在“直方图”选项卡中什么都没有显示：
我在这里想念什么？我是否正确记录了梯度？
解决方法
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