我正在使用TensorFlow-2.2，tensorflow_model_optimization和Python 3.8。我正在尝试量化和训练包含稀疏度为91.3375％的LeNet-300-100密集神经网络。这意味着91.3375％的权重为零。我一直关注Quantization TF tutorial，我想训练这样一个稀疏的网络，该网络已使用 tf.GradientTape 而不是 q_aware_model.fit（）进行了量化。 >
如果您查看example code，则相关的代码段为：
quantize_model = tfmot.quantization.keras.quantize_model
# q_aware stands for for quantization aware.
q_aware_model = quantize_model(model)
# 'quantize_model' requires recompilation-
q_aware_model.compile(
optimizer = tf.keras.optimizers.Adam(lr = 0.0012),
loss=tf.keras.losses.categorical_crossentropy,
metrics=['accuracy']
)
# Define 'train_one_step()' and 'test_step()' functions here-
@tf.function
def train_one_step(model, mask_model, optimizer, x, y):
'''
Function to compute one step of gradient descent optimization
'''
with tf.GradientTape() as tape:
# Make predictions using defined model-
y_pred = model(x)
# Compute loss-
loss = loss_fn(y, y_pred)
# Compute gradients wrt defined loss and weights and biases-
grads = tape.gradient(loss, model.trainable_variables)
# type(grads)
# list
# List to hold element-wise multiplication between-
# computed gradient and masks-
grad_mask_mul = []
# Perform element-wise multiplication between computed gradients and masks-
for grad_layer, mask in zip(grads, mask_model.trainable_weights):
grad_mask_mul.append(tf.math.multiply(grad_layer, mask))
# Apply computed gradients to model's weights and biases-
optimizer.apply_gradients(zip(grad_mask_mul, model.trainable_variables))
# Compute accuracy-
train_loss(loss)
train_accuracy(y, y_pred)
return None
@tf.function
def test_step(model, optimizer, data, labels):
"""
Function to test model performance
on testing dataset
"""
predictions = model(data)
t_loss = loss_fn(labels, predictions)
test_loss(t_loss)
test_accuracy(labels, predictions)
return None
# Train model using 'GradientTape'-
# Initialize parameters for Early Stopping manual implementation-
# best_val_loss = 100
# loc_patience = 0
for epoch in range(num_epochs):
if loc_patience >= patience:
print("\n'EarlyStopping' called!\n")
break
# Reset the metrics at the start of the next epoch
train_loss.reset_states()
train_accuracy.reset_states()
test_loss.reset_states()
test_accuracy.reset_states()
for x, y in train_dataset:
train_one_step(q_aware_model, mask_model, optimizer, x, y)
for x_t, y_t in test_dataset:
test_step(q_aware_model, optimizer, x_t, y_t)
template = 'Epoch {0}, Loss: {1:.4f}, Accuracy: {2:.4f}, Test Loss: {3:.4f}, Test Accuracy: {4:4f}'
'''
# 'i' is the index for number of pruning rounds-
history_main[i]['accuracy'][epoch] = train_accuracy.result() * 100
history_main[i]['loss'][epoch] = train_loss.result()
history_main[i]['val_loss'][epoch] = test_loss.result()
history_main[i]['val_accuracy'][epoch] = test_accuracy.result() * 100
'''
print(template.format(
epoch + 1, train_loss.result(),
train_accuracy.result()*100, test_loss.result(),
test_accuracy.result()*100)
)
# Count number of non-zero parameters in each layer and in total-
# print("layer-wise manner model, number of nonzero parameters in each layer are: \n")
model_sum_params = 0
for layer in winning_ticket_model.trainable_weights:
# print(tf.math.count_nonzero(layer, axis = None).numpy())
model_sum_params += tf.math.count_nonzero(layer, axis = None).numpy()
print("Total number of trainable parameters = {0}\n".format(model_sum_params))
# Code for manual Early Stopping:
if np.abs(test_loss.result() < best_val_loss) >= minimum_delta:
# update 'best_val_loss' variable to lowest loss encountered so far-
best_val_loss = test_loss.result()
# reset 'loc_patience' variable-
loc_patience = 0
else: # there is no improvement in monitored metric 'val_loss'
loc_patience += 1 # number of epochs without any improvement
给出以下错误：
--------------------------------------------------- ---------------------------- InvalidArgumentError错误回溯（最近的调用 最后） 19 train_dataset中的x，y为20： ---> 21 train_one_step（q_aware_model，mask_model，optimizer，x，y） 22 23
〜/ .local / lib / python3.8 / site-packages / tensorflow / python / eager / def_function.py 在通话中（自己，* args，** kwds） 578 xla_context.Exit（） 其他579 -> 580结果= self._call（* args，** kwds） 581 582如果tracing_count == self._get_tracing_count（）：
〜/ .local / lib / python3.8 / site-packages / tensorflow / python / eager / def_function.py 在_call（self，* args，** kwds）中 642＃解除成功，因此变量被初始化，我们可以运行 643＃无状态功能。 -> 644返回self._stateless_fn（* args，** kwds） 第645章 646 canon_args，canon_kwds = \
〜/ .local / lib / python3.8 / site-packages / tensorflow / python / eager / function.py 在呼叫（自我，* args，** kwargs）2418中与self._lock：
2419 graph_function，args，kwargs = self._maybe_define_function（args，kwargs） -> 2420返回graph_function._filtered_call（args，kwargs）＃pylint：disable = protected-access 2421 2422 @property〜/ .local / lib / python3.8 / site-packages / tensorflow / python / eager / function.py 在_filtered_call（self，args，kwargs）1659
args中kwargs。 1660“”“ -> 1661返回self._call_flat（1662（t为nest.flatten（（args，kwargs），expand_composites = True）中的t）1663
如果isinstance（t，（ops.Tensor，〜/ .local / lib / python3.8 / site-packages / tensorflow / python / eager / function.py 在_call_flat（自己，args，captured_inputs，cancel_manager）中
1743 and execute_eagerly）：1744＃没有磁带 观看;跳至运行该功能。 -> 1745返回self._build_call_outputs（self._inference_function.call（1746
ctx，args，cancel_manager = cancellation_manager））1747
forward_backward = self._select_forward_and_backward_functions（〜/ .local / lib / python3.8 / site-packages / tensorflow / python / eager / function.py 在通话中（self，ctx，args，cancel_manager） 591带有_InterpolateFunctionError（）： 第592章 -> 593个输出= execute.execute（ 第594章（二更） 595 num_outputs = self._num_outputs，
〜/ .local / lib / python3.8 / site-packages / tensorflow / python / eager / execute.py 在quick_execute（op_name，num_outputs，输入，attrs，ctx，name）中 57试试： 58 ctx.ensure_initialized（） ---> 59张量= pywrap_tfe.TFE_Py_Execute（ctx._handle，device_name，op_name， 60个输入，attrs，num_outputs） 61，除了core._NotOkStatusException如e：
InvalidArgumentError：var和grad形状不同[10] [100,10] [[节点Adam / Adam / update_4 / ResourceApplyAdam（定义为 ：29）]] [Op：__ inference_train_one_step_20360]
错误可能源于输入操作。输入源 连接到节点Adam / Adam / update_4 / ResourceApplyAdam的操作： Mul_4（定义为：26）
顺序/ quant_dense_2 / BiasAdd / ReadVariableOp /资源（定义为 /home/arjun/.local/lib/python3.8/site-packages/tensorflow_model_optimization/python/core/quantization/keras/quantize_wrapper.py:162）函数调用堆栈：train_one_step
有没有办法将TF模型量化与tf.GradientTape结合起来？
谢谢！