问题描述
下面的代码片段是 TensorFlow 模型的 vanila 实现,其中我使用了子类模型和自定义拟合函数(通过 train_step
和 test_step
实现)。代码在 Eager Execution 模式下(TF2.0 中的默认执行模式)运行良好,但在图形模式下失败。
import numpy as np
import tensorflow as tf
class Encoder(tf.keras.Model):
def __init__(self):
super(Encoder,self).__init__(name = 'Encoder')
self.input_layer = tf.keras.layers.Dense(10)
self.hidden_layer1 = tf.keras.layers.Dense(10)
self.dropout_laye1 = tf.keras.layers.Dropout(0.2)
self.hidden_layer2 = tf.keras.layers.Dense(10)
self.dropout_laye2 = tf.keras.layers.Dropout(0.2)
self.hidden_layer3 = tf.keras.layers.Dense(10)
self.dropout_laye3 = tf.keras.layers.Dropout(0.2)
self.output_layer = tf.keras.layers.Dense(1)
def call(self,input_data,training):
fx = self.input_layer(input_data)
fx = self.hidden_layer1(fx)
if training:
fx = self.dropout_laye1(fx)
fx = self.hidden_layer2(fx)
if training:
fx = self.dropout_laye2(fx)
fx = self.hidden_layer3(fx)
if training:
fx = self.dropout_laye3(fx)
return self.output_layer(fx)
class CustomModelV1(tf.keras.Model):
def __init__(self):
super(CustomModelV1,self).__init__()
self.encoder = Encoder()
def train_step(self,data):
# Unpack the data. Its structure depends on your model and
# on what you pass to `fit()`.
x,y = data
with tf.GradientTape() as tape:
y_pred = self.encoder(x,training=True) # Forward pass
# Compute the loss value
# (the loss function is configured in `compile()`)
loss = self.compiled_loss(y,y_pred,regularization_losses=self.losses)
# Compute gradients
trainable_vars = self.trainable_variables
gradients = tape.gradient(loss,trainable_vars)
# Update weights
self.optimizer.apply_gradients(zip(gradients,trainable_vars))
# Update metrics (includes the metric that tracks the loss)
self.compiled_metrics.update_state(y,y_pred)
# Return a dict mapping metric names to current value
return {m.name: m.result() for m in self.metrics}
# Just use `fit` as usual
x = tf.data.Dataset.from_tensor_slices(np.random.random((1000,32)))
y_numpy = np.random.random((1000,1))
y = tf.data.Dataset.from_tensor_slices(y_numpy)
x_window = x.window(30,shift=10,stride=1)
flat_x = x_window.flat_map(lambda t: t)
flat_x_scaled = flat_x.map(lambda t: t * 2)
y_window = y.window(30,stride=1)
flat_y = y_window.flat_map(lambda t: t)
flat_y_scaled = flat_y.map(lambda t: t * 2)
z = tf.data.Dataset.zip((flat_x_scaled,flat_y_scaled)).batch(32).cache().shuffle(buffer_size=32).prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
# Construct and compile an instance of CustomModel
model = CustomModelV1()
model.compile(optimizer="adam",loss="mse",metrics=["mae"])
model.fit(z,epochs=3)
该代码在 Eager 模式下运行良好,但在图形模式下会引发以下错误。我使用 tf.compat.v1.disable_eager_execution()
禁用了急切执行。
AttributeError Traceback (most recent call last)
<ipython-input-4-f7a5b420f08f> in <module>
27 model.compile(optimizer="adam",metrics=["mae"])
28
---> 29 model.fit(z,epochs=3)
~\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\keras\engine\training_v1.py in fit(self,x,y,batch_size,epochs,verbose,callbacks,validation_split,validation_data,shuffle,class_weight,sample_weight,initial_epoch,steps_per_epoch,validation_steps,validation_freq,max_queue_size,workers,use_multiprocessing,**kwargs)
793
794 func = self._select_training_loop(x)
--> 795 return func.fit(
796 self,797 x=x,~\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\keras\engine\training_arrays_v1.py in fit(self,model,**kwargs)
617 steps_per_epoch,x)
618
--> 619 x,sample_weights = model._standardize_user_data(
620 x,621 y,~\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\keras\engine\training_v1.py in _standardize_user_data(self,check_steps,steps_name,steps,extract_tensors_from_dataset)
2328 is_compile_called = False
2329 if not self._is_compiled and self.optimizer:
-> 2330 self._compile_from_inputs(all_inputs,y_input,y)
2331 is_compile_called = True
2332
~\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\keras\engine\training_v1.py in _compile_from_inputs(self,all_inputs,target,orig_inputs,orig_target)
2548 # We need to use `y` to set the model targets.
2549 if training_utils_v1.has_tensors(target):
-> 2550 target = training_utils_v1.cast_if_floating_dtype_and_mismatch(
2551 target,self.outputs)
2552 training_utils_v1.validate_input_types(
~\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\keras\engine\training_utils_v1.py in cast_if_floating_dtype_and_mismatch(targets,outputs)
1377 if tensor_util.is_tf_type(targets):
1378 # There is one target,so output[0] should be the only output.
-> 1379 return cast_single_tensor(targets,dtype=outputs[0].dtype)
1380 new_targets = []
1381 for target,out in zip(targets,outputs):
AttributeError: 'nonetype' object has no attribute 'dtype'
解决方法
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