问题描述
我正在以 Eager Execution 模式和图形模式训练以下模型。该模型在 Eager Execution 模式下训练良好，但在图形模式下无限期运行。我尝试以多种方式调试，但没有成功。
class CustomModelV2(tf.keras.Model):
def __init__(self):
super(CustomModelV2,self).__init__()
self.encoder = Encoder(32)
self.encoder.build(input_shape=(None,32))
self.loss_tracker = tf.keras.metrics.Mean(name="loss")
def call(self,inputs,training):
return self.encoder(inputs,training)
@property
def metrics(self):
# We list our `Metric` objects here so that `reset_states()` can be
# called automatically at the start of each epoch
# or at the start of `evaluate()`.
# If you don't implement this property,you have to call
# `reset_states()` yourself at the time of your choosing.
return [self.loss_tracker]
@tf.function
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.call(x,training=True) # Forward pass
# Compute the loss value
# (the loss function is configured in `compile()`)
r_loss = tf.keras.losses.mean_squared_error(y,y_pred)
loss = r_loss
# 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.loss_tracker.update_state(loss)
# Return a dict mapping metric names to current value
return {"loss": self.loss_tracker.result()}
class Encoder(tf.keras.Model):
def __init__(self,input_size):
super(Encoder,self).__init__(name = 'Encoder')
self.input_layer = DenseLayer(128,input_size,0.0,'float32')
self.hidden_layer1 = DenseLayer(128,128,0.001,'float32')
self.dropout_laye1 = tf.keras.layers.Dropout(0.2)
self.hidden_layer2 = DenseLayer(64,'float32')
self.dropout_laye2 = tf.keras.layers.Dropout(0.2)
self.hidden_layer3 = DenseLayer(64,64,'float32')
self.dropout_laye3 = tf.keras.layers.Dropout(0.2)
self.output_layer = LinearLayer(64,'float32')
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 LinearLayer(tf.keras.layers.Layer):
def __init__(self,units,input_dim,weights_regularizer,bias_regularizer,d_type):
super(LinearLayer,self).__init__()
self.w = self.add_weight(name='w_linear',shape = (input_dim,units),initializer = tf.keras.initializers.RandomUniform(
minval=-tf.cast(tf.math.sqrt(6/(input_dim+units)),dtype = d_type),maxval=tf.cast(tf.math.sqrt(6/(input_dim+units)),seed=16751),regularizer = tf.keras.regularizers.l1(weights_regularizer),trainable = True)
self.b = self.add_weight(name='b_linear',shape = (units,),initializer = tf.zeros_initializer(),regularizer = tf.keras.regularizers.l1(bias_regularizer),trainable = True)
def call(self,inputs):
return tf.matmul(inputs,self.w) + self.b
class DenseLayer(tf.keras.layers.Layer):
def __init__(self,d_type):
super(DenseLayer,self).__init__()
self.w = self.add_weight(name='w_dense',initializer = tf.keras.initializers.RandomUniform(
minval=-tf.cast(tf.math.sqrt(6.0/(input_dim+units)),maxval=tf.cast(tf.math.sqrt(6.0/(input_dim+units)),trainable = True)
self.b = self.add_weight(name='b_dense',inputs):
x = tf.matmul(inputs,self.w) + self.b
return tf.nn.elu(x)
以下是训练模型的脚本：
# Just use `fit` as usual
x = tf.data.Dataset.from_tensor_slices(np.random.random((5000,32)))
y_numpy = np.random.random((5000,1))
y_numpy[:,3:] = None
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)
# Stopping criteria if the training loss doesn't go down by 1e-3
early_stop_cb = tf.keras.callbacks.EarlyStopping(
monitor='loss',min_delta = 1e-3,verbose = 1,mode='min',patience = 3,baseline=None,restore_best_weights=True)
# Construct and compile an instance of CustomModel
model = CustomModelV2()
model.compile(optimizer=tf.optimizers.Adagrad(0.01))
history = model.fit(z,epochs=3,callbacks=[early_stop_cb])
以下是图形模式下的输出：
WARNING:tensorflow:Output output_1 missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to output_1.
WARNING:tensorflow:From C:\Users\jain432\Anaconda3\envs\tf\lib\site-packages\tensorflow\python\keras\optimizer_v2\adagrad.py:87: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
Train on None steps
Epoch 1/3
479916/UnkNown - 667s 1ms/step - batch: 239957.5000 - size: 1.0000 - loss: 2.1716e-04
正如我所说，在急切执行模式下工作正常：
Epoch 1/3
468/468 [==============================] - 2s 3ms/step - loss: 0.4173
Epoch 2/3
468/468 [==============================] - 1s 3ms/step - loss: 0.3695
Epoch 3/3
468/468 [==============================] - 1s 3ms/step - loss: 0.3608
有人能帮我了解这里发生了什么以及我哪里做错了吗？
解决方法
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