我是机器学习和tensorflow的新手，所以我正在从事一个个人项目，以尝试学习一些东西。我现在遇到此错误，而且我在网上进行的所有研究都没有帮助我准确地缩小问题的范围。我最好的猜测是，我无法理解某些事情正在使我的损失函数不可微，这对于RNN来说是个问题。感谢您的帮助！
$ python3 Stocker.py APIKEY MSFT
ValueError: No gradients provided for any variable: ['lstm/lstm_cell/kernel:0', 'lstm/lstm_cell/recurrent_kernel:0', 'lstm/lstm_cell/bias:0', 'dense/kernel:0', 'dense/bias:0']
代码：
import pandas as pd
import numpy as np
import argparse
import tensorflow as tf
from matplotlib import pyplot
import data_helpers as dh
class Stocker:
def __init__(self, training, test, loss='mse', optimizer='adam'):
""" Creating Stocker instance immediately creates model
Model (WIP) is a two-layer LSTM. Defaults to Mean Squared Error
loss function and ADAM optimizer function.
"""
self.training_data = training
self.test_data = test
self.model = tf.keras.Sequential()
self.model.add(tf.keras.layers.LSTM(100, activation='tanh', recurrent_activation='sigmoid', \
input_shape=(training.shape[1], training.shape[2])))
self.model.add(tf.keras.layers.Dense(5))
self.model.compile(loss=loss, optimizer=optimizer)
print(self.model.summary())
def train(self):
self.fit = self.model.fit(self.training_data, epochs=50, \
batch_size=100, \
validation_data=self.test_data, verbose=2, shuffle=False)
pyplot.plot(self.fit['loss'], label='train')
pyplot.plot(self.fit['val_loss'], label='test')
pyplot.legend()
pyplot.show()
if __name__ == '__main__':
""" Test/Demo of Stocker module """
parser = argparse.ArgumentParser(description="Model Training Script")
parser.add_argument('key', help='User API Key')
parser.add_argument('-outdir', metavar='out', default='/models/', help="Directory for stored model(s) (one for each symbol).")
parser.add_argument('symbols', nargs=argparse.REMAINDER, help="List of symbols to train (Place all at end of command)")
parse = parser.parse_args()
data = {}
for symbol in parse.symbols:
# read historical daily data from alpha_vantage
# store in python dict
hist = dh.daily(symbol, parse.key, compact=False)
data[symbol] = hist
print(hist)
#print()
""" Data Preprocessing """
# turn dataframe to numpy array
tmp = hist.to_numpy()
# split into training and testing sets 90-10
split = round(tmp.shape[0]*1/10)
test, training = tmp[:split], tmp[split:]
test = dh.array_to_supervised(test, 3)
test = test.reshape((test.shape[0], 1, test.shape[1]))
training = dh.array_to_supervised(training, 3)
training = training.reshape((training.shape[0], 1, training.shape[1]))
# convert numpy arrays to tensors and reshape for LSTM
training_tensor = tf.convert_to_tensor(training, np.float32)
test_tensor = tf.convert_to_tensor(test, np.float32)
""" -------------------------------- """
#print(training_tensor)
#print(test_tensor)
model = Stocker(training_tensor, test_tensor)
model.train()
答案 0 :(得分：1)
仅传递训练数据而错过了通过model.fit()中的标签时，会出现此错误。我可以使用以下代码重新创建您的错误。您可以从here下载程序中我正在使用的数据集。我正在使用Tensorflow version 2.2.0。
重新创建问题的代码-
%tensorflow_version 2.x
# MLP for Pima Indians Dataset saved to single file
import numpy as np
from numpy import loadtxt
import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
# load pima indians dataset
dataset = np.loadtxt("/content/pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# define model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Model Summary
#model.summary()
# Fit the model
model.fit(X, epochs=150, batch_size=10, verbose=0)
输出-
2.2.0
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-4-7ddca8f2992e> in <module>()
28
29 # Fit the model
---> 30 model.fit(X, epochs=150, batch_size=10, verbose=0)
10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
966 except Exception as e: # pylint:disable=broad-except
967 if hasattr(e, "ag_error_metadata"):
--> 968 raise e.ag_error_metadata.to_exception(e)
969 else:
970 raise
ValueError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:571 train_function *
outputs = self.distribute_strategy.run(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 run **
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:541 train_step **
self.trainable_variables)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1804 _minimize
trainable_variables))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:521 _aggregate_gradients
filtered_grads_and_vars = _filter_grads(grads_and_vars)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:1219 _filter_grads
([v.name for _, v in grads_and_vars],))
ValueError: No gradients provided for any variable: ['dense_5/kernel:0', 'dense_5/bias:0', 'dense_6/kernel:0', 'dense_6/bias:0', 'dense_7/kernel:0', 'dense_7/bias:0'].
解决方案-通过model.fit()中的培训标签，您的错误将得到解决。
已修改
model.fit(X , epochs=150, batch_size=10, verbose=0)
到
model.fit(X , Y, epochs=150, batch_size=10, verbose=0)
代码-
%tensorflow_version 2.x
# MLP for Pima Indians Dataset saved to single file
import numpy as np
from numpy import loadtxt
import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
# load pima indians dataset
dataset = np.loadtxt("/content/pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# define model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Model Summary
#model.summary()
# Fit the model
model.fit(X , Y, epochs=150, batch_size=10, verbose=0)
输出-
2.2.0
<tensorflow.python.keras.callbacks.History at 0x7f9208433eb8>
希望这能回答您的问题。学习愉快。