TF2:在非急切模式下的keras回调中计算渐变

Question

TF版本：2.2.0-rc3(在Colab中(

我在回调中使用以下代码(来自tf.keras，在训练期间获得计算的梯度(来计算模型中所有参数的梯度。

def on_train_begin(self, logs=None):
# Functions return weights of each layer
self.layerweights = []
for lndx, l in enumerate(self.model.layers):
if hasattr(l, 'kernel'):
self.layerweights.append(l.kernel)
input_tensors = [self.model.inputs[0],
self.model.sample_weights[0],
self.model.targets[0],
K.learning_phase()]
# Get gradients of all the relevant layers at once
grads = self.model.optimizer.get_gradients(self.model.total_loss, self.layerweights)
self.get_gradients = K.function(inputs=input_tensors,outputs=grads)

但是，当我运行此程序时，会出现以下错误。

AttributeError: 'Model' object has no attribute 'sample_weights'

对于model.targets，同样的错误也正在发生。

如何获取回调中的梯度？

在急切模式下，Keras Tensorflow 2.0的Get Gradients解决方案可以工作。但是，我想在非急切模式下使用它。

Answer 1

以下是使用keras后端捕获梯度的端到端代码。我从model.fit的回调中调用了梯度捕获函数，用于在每个历元结束后捕获梯度这段代码在tensorflow 1.x和tensorflow 2.x版本中都是兼容的，我也在colab中运行过它如果要在tensorflow 1.x中运行，请将程序中的第一条语句替换为%tensorflow_version 1.x，然后重新启动运行时。

捕捉模型的梯度-

# Importing dependency
%tensorflow_version 2.x
from tensorflow import keras
from tensorflow.keras import backend as K
from tensorflow.keras import datasets
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.layers import BatchNormalization
import numpy as np
import tensorflow as tf
tf.keras.backend.clear_session()  # For easy reset of notebook state.
tf.compat.v1.disable_eager_execution()
# Import Data
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
# Build Model
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(10))
# Model Summary
model.summary()
# Model Compile 
model.compile(optimizer='adam',
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# Define the Gradient Fucntion
epoch_gradient = []
# Define the Gradient Function
def get_gradient_func(model):
grads = K.gradients(model.total_loss, model.trainable_weights)
inputs = model._feed_inputs + model._feed_targets + model._feed_sample_weights
func = K.function(inputs, grads)
return func
# Define the Required Callback Function
class GradientCalcCallback(keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs=None):
get_gradient = get_gradient_func(model)
grads = get_gradient([train_images, train_labels, np.ones(len(train_labels))])
epoch_gradient.append(grads)
epoch = 4
model.fit(train_images, train_labels, epochs=epoch, validation_data=(test_images, test_labels), callbacks=[GradientCalcCallback()])

# (7) Convert to a 2 dimensiaonal array of (epoch, gradients) type
gradient = np.asarray(epoch_gradient)
print("Total number of epochs run:", epoch)
print("Gradient Array has the shape:",gradient.shape)

输出-

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 30, 30, 32)        896       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 15, 15, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 13, 13, 64)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64)          0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 4, 4, 64)          36928     
_________________________________________________________________
flatten (Flatten)            (None, 1024)              0         
_________________________________________________________________
dense (Dense)                (None, 64)                65600     
_________________________________________________________________
dense_1 (Dense)              (None, 10)                650       
=================================================================
Total params: 122,570
Trainable params: 122,570
Non-trainable params: 0
_________________________________________________________________
Train on 50000 samples, validate on 10000 samples
Epoch 1/4
50000/50000 [==============================] - 73s 1ms/sample - loss: 1.8199 - accuracy: 0.3834 - val_loss: 1.4791 - val_accuracy: 0.4548
Epoch 2/4
50000/50000 [==============================] - 357s 7ms/sample - loss: 1.3590 - accuracy: 0.5124 - val_loss: 1.2661 - val_accuracy: 0.5520
Epoch 3/4
50000/50000 [==============================] - 377s 8ms/sample - loss: 1.1981 - accuracy: 0.5787 - val_loss: 1.2625 - val_accuracy: 0.5674
Epoch 4/4
50000/50000 [==============================] - 345s 7ms/sample - loss: 1.0838 - accuracy: 0.6183 - val_loss: 1.1302 - val_accuracy: 0.6083
Total number of epochs run: 4
Gradient Array has the shape: (4, 10)

希望这能回答你的问题。快乐学习。