【发布时间】：2020-09-30 17:04:54
【问题描述】：
我是机器学习和 tensorflow 的新手，所以我现在正在做一个个人项目来尝试学习一点。我现在遇到了这个错误，我所有的在线研究并没有帮助我准确地缩小问题范围。我最好的猜测是，在幕后发生了一些我无法识别的事情，这使得我的损失函数不可微，我知道这对 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()
【问题讨论】：
标签： python tensorflow keras lstm