我正在尝试使用子类化 API 构建自定义 keras 模型，但是当我加载模型的前一个实例并尝试对其进行训练时出现一些错误：
这是我的模型的类，它有 3 个输入和 1 个输出：
import tensorflow as tf
spec1 = tf.TensorSpec(shape=(1,40,5,1))
spec2 = tf.TensorSpec(shape=(1,3))
class Conv_Rnn_model(tf.keras.Model):
def __init__(self):
# super() permet d'appeler le constructeur de la classe mère dans la classe fille
# permet égalemet de résoudre le problème d'appel multiple de méthode dans les configuration de classe en diamant
super().__init__()
self.loss_object = tf.keras.losses.MeanSquaredError()
self.optimizer = tf.keras.optimizers.Adam()
# Convolutions :
# input : [batch_size, rows, cols, channels]
# return : [batch_size, new_rows, new_cols, filters]
self.conv1 = tf.keras.layers.Conv2D(filters = 32, kernel_size = (8,2),input_shape=(40,5,1), activation='relu', name ="conv1")
self.conv2 = tf.keras.layers.Conv2D(filters = 64, kernel_size = (6,1),input_shape=(40,5,1), activation='relu', name ="conv2")
self.conv3 = tf.keras.layers.Conv2D(filters =128, kernel_size = (6,1),input_shape=(40,5,1), activation='relu', name ="conv3")
# recurrent cells :
#input : [batch_size, time_steps, features]
#return : [batch_size, time_steps, units (if return_sequence=True)]
self.lstm1A = tf.keras.layers.LSTM(64, return_sequences=True, name = "lstm1A")
self.lstm1B = tf.keras.layers.LSTM(64, name = "lstm1B")
self.lstm2A = tf.keras.layers.LSTM(64, return_sequences=True, name = "lstm2A")
self.lstm2B = tf.keras.layers.LSTM(64, name = "lstm2B")
self.lstm3A = tf.keras.layers.LSTM(64, return_sequences=True, name = "lstm3A")
self.lstm3B = tf.keras.layers.LSTM(64, name = "lstm3B")
# Concat layer :
self.concat = tf.keras.layers.Concatenate(axis=1)
# fully connected layers :
#input : [batch_size, ... , input_dim]
#return : [batch_size, ... , units]
self.dense = tf.keras.layers.Dense(32)
self.out = tf.keras.layers.Dense(3, activation='softmax')
@tf.function(input_signature=[[tf.TensorSpec(shape=(1,40,5,1),name="M15"),
tf.TensorSpec(shape=(1,40,5,1),name="H1"),
tf.TensorSpec(shape=(1,40,5,1),name="H4")]
])
def call(self, data):
"""
TODO: comprendre comment se calculer les outputs des conv2D
pour remplacer les conv_res.shape par des constantes
"""
#tf.no_gradient("concatenate")
#tf.no_gradient("reshape")
conv1_res = self.conv1(data[0])
conv2_res = self.conv2(data[1])
conv3_res = self.conv3(data[2])
shape1 = (1, conv1_res.shape[1],conv1_res.shape[2]*conv1_res.shape[3])
shape2 = (1, conv2_res.shape[1],conv2_res.shape[2]*conv2_res.shape[3])
shape3 = (1, conv3_res.shape[1],conv3_res.shape[2]*conv3_res.shape[3])
f1 = self.lstm1B(self.lstm1A( tf.reshape(conv1_res, shape1) ))
f2 = self.lstm2B(self.lstm2A( tf.reshape(conv2_res, shape2) ))
f3 = self.lstm3B(self.lstm3A( tf.reshape(conv3_res, shape3) ))
# returns of fully connected layers
pre_output = self.dense(self.concat([f1,f2,f3]))
output = self.out(pre_output)
return output
@tf.function(input_signature=[[spec1,spec1,spec1], spec2])
def train_step(self,X_train, y_train):
#X_train, y_train = data
with tf.GradientTape() as tape:
y_pred = self(X_train) # Forward pass
# Compute the loss value
loss = self.loss_object(y_train, y_pred)
# Compute gradients
gradients = tape.gradient(loss, self.trainable_variables)
# Update weights
self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))
当我训练我的模型然后我保存它时，一切都很好：
from Conv_Rnn_model import Conv_Rnn_model
from tensorflow.keras.models import load_model
from numpy.random import rand
from functions import getDataset_3period
model_1 = Conv_Rnn_model()
model_1.compile(loss='mse', optimizer='adam')
iterations = 5
data_environment = getDataset_3period(window_size=40)# get dataframe of EURUSD
for i in range(iterations):
state = state = data_environment[i]
# target = tf.constant(rand(1,3),dtype=tf.float32)
target= rand(1,3)
X_train = [state[:1],state[1:2],state[2:3]]
# X_train = tf.constant(X_train, dtype=tf.float32)
model_1.train_step(X_train, target)
print("epoch", i)
model_1.save("models/model_test1")
但是当我尝试重新加载我训练过的模型并再次训练它时，我得到了错误：
model_2 = load_model("models/model_test1", compile=False)
2020-08-08 18:17:27.277841: W tensorflow/core/common_runtime/graph_constructor.cc:808] Node 'while' has 11 outputs but the _output_shapes attribute specifies shapes for 20 outputs. Output shapes may be inaccurate.
2020-08-08 18:17:28.048269: W tensorflow/core/common_runtime/graph_constructor.cc:808] Node 'while' has 11 outputs but the _output_shapes attribute specifies shapes for 20 outputs. Output shapes may be inaccurate.
2020-08-08 18:17:28.651946: W tensorflow/core/common_runtime/graph_constructor.cc:808] Node 'while' has 11 outputs but the _output_shapes attribute specifies shapes for 20 outputs. Output shapes may be inaccurate.
2020-08-08 18:17:28.946418: W tensorflow/core/common_runtime/graph_constructor.cc:808] Node 'while' has 11 outputs but the _output_shapes attribute specifies shapes for 20 outputs. Output shapes may be inaccurate.
2020-08-08 18:17:32.857832: W tensorflow/core/common_runtime/graph_constructor.cc:808] Node 'while' has 11 outputs but the _output_shapes attribute specifies shapes for 20 outputs. Output shapes may be inaccurate.
2020-08-08 18:17:32.872207: W tensorflow/core/common_runtime/graph_constructor.cc:808] Node 'while' has 11 outputs but the _output_shapes attribute specifies shapes for 20 outputs. Output shapes may be inaccurate.
2020-08-08 18:17:32.891483: W tensorflow/core/common_runtime/graph_constructor.cc:808] Node 'lstm1A/PartitionedCall' has 5 outputs but the _output_shapes attribute specifies shapes for 22 outputs. Output shapes may be inaccurate.
2020-08-08 18:17:32.892203: W tensorflow/core/common_runtime/graph_constructor.cc:808] Node 'lstm1B/PartitionedCall' has 5 outputs but the _output_shapes attribute specifies shapes for 22 outputs. Output shapes may be inaccurate.
2020-08-08 18:17:32.892926: W tensorflow/core/common_runtime/graph_constructor.cc:808] Node 'lstm2A/PartitionedCall' has 5 outputs but the _output_shapes attribute specifies shapes for 22 outputs. Output shapes may be inaccurate.
2020-08-08 18:17:32.893593: W tensorflow/core/common_runtime/graph_constructor.cc:808] Node 'lstm2B/PartitionedCall' has 5 outputs but the _output_shapes attribute specifies shapes for 22 outputs. Output shapes may be inaccurate.
2020-08-08 18:17:32.894289: W tensorflow/core/common_runtime/graph_constructor.cc:808] Node 'lstm3A/PartitionedCall' has 5 outputs but the _output_shapes attribute specifies shapes for 22 outputs. Output shapes may be inaccurate.
2020-08-08 18:17:32.894950: W tensorflow/core/common_runtime/graph_constructor.cc:808] Node 'lstm3B/PartitionedCall' has 5 outputs but the _output_shapes attribute specifies shapes for 22 outputs. Output shapes may be inaccurate.
model_2(X_train)
Out[3]: <tf.Tensor: shape=(1, 3), dtype=float32, numpy=array([[0.2654996 , 0.40409103, 0.33040944]], dtype=float32)>
带 train_step 功能：
model_2.train_step(X_train, target)
Traceback (most recent call last):
File "<ipython-input-4-27db33666dda>", line 1, in <module>
model_2.train_step(X_train, target)
TypeError: train_step() takes 2 positional arguments but 3 were given
或具有拟合功能：
model_2.compile(loss='mse', optimizer='adam')
model_2.fit(X_train, target)
Traceback (most recent call last):
File "<ipython-input-6-d013b7a5a810>", line 2, in <module>
model_2.fit(X_train, target)
File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 103, in _method_wrapper
return method(self, *args, **kwargs)
File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1102, in fit
tmp_logs = self.train_function(iterator)
File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 787, in __call__
result = self._call(*args, **kwds)
File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 830, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 702, in _initialize
self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2948, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 3319, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 3171, in _create_graph_function
func_graph_module.func_graph_from_py_func(
File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 987, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 613, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 974, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:809 train_function *
return step_function(self, iterator)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:799 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1261 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2794 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3217 _call_for_each_replica
return fn(*args, **kwargs)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:792 run_step **
outputs = model.train_step(data)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:750 train_step
y_pred = self(x, training=True)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:990 __call__
outputs = call_fn(inputs, *args, **kwargs)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/utils.py:71 return_outputs_and_add_losses
outputs, losses = fn(inputs, *args, **kwargs)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/utils.py:167 wrap_with_training_arg
return control_flow_util.smart_cond(
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/utils/control_flow_util.py:112 smart_cond
return smart_module.smart_cond(
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/smart_cond.py:54 smart_cond
return true_fn()
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/utils.py:168 <lambda>
training, lambda: replace_training_and_call(True),
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/utils.py:165 replace_training_and_call
return wrapped_call(*args, **kwargs)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:787 __call__
result = self._call(*args, **kwds)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:821 _call
results = self._stateful_fn(*args, **kwds)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py:2921 __call__
graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py:3319 _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py:3171 _create_graph_function
func_graph_module.func_graph_from_py_func(
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:987 func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:613 wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/saved_model/function_deserialization.py:251 restored_function_body
raise ValueError(
ValueError: Could not find matching function to call loaded from the SavedModel. Got:
Positional arguments (2 total):
* (<tf.Tensor 'data:0' shape=(None, 40, 5, 1) dtype=float32>, <tf.Tensor 'data_1:0' shape=(None, 40, 5, 1) dtype=float32>, <tf.Tensor 'data_2:0' shape=(None, 40, 5, 1) dtype=float32>)
* True
Keyword arguments: {}
Expected these arguments to match one of the following 4 option(s):
Option 1:
Positional arguments (2 total):
* [TensorSpec(shape=(1, 40, 5, 1), dtype=tf.float32, name='data/0'), TensorSpec(shape=(1, 40, 5, 1), dtype=tf.float32, name='data/1'), TensorSpec(shape=(1, 40, 5, 1), dtype=tf.float32, name='data/2')]
* False
Keyword arguments: {}
Option 2:
Positional arguments (2 total):
* [TensorSpec(shape=(1, 40, 5, 1), dtype=tf.float32, name='M15'), TensorSpec(shape=(1, 40, 5, 1), dtype=tf.float32, name='H1'), TensorSpec(shape=(1, 40, 5, 1), dtype=tf.float32, name='H4')]
* False
Keyword arguments: {}
Option 3:
Positional arguments (2 total):
* [TensorSpec(shape=(1, 40, 5, 1), dtype=tf.float32, name='M15'), TensorSpec(shape=(1, 40, 5, 1), dtype=tf.float32, name='H1'), TensorSpec(shape=(1, 40, 5, 1), dtype=tf.float32, name='H4')]
* True
Keyword arguments: {}
Option 4:
Positional arguments (2 total):
* [TensorSpec(shape=(1, 40, 5, 1), dtype=tf.float32, name='data/0'), TensorSpec(shape=(1, 40, 5, 1), dtype=tf.float32, name='data/1'), TensorSpec(shape=(1, 40, 5, 1), dtype=tf.float32, name='data/2')]
* True
Keyword arguments: {}
自 1 周以来，我一直在尝试解决此错误，并且我已经多次阅读 tensorflow 指南。