我使用tensorflow从头开始编写单层LSTM RNN的代码。 当我调用sess.run（）并给出feed_dict时，我收到错误。 我在sess.run（）命令之前使用了pdb_set_trace（），因此在pdb跟踪中输入“n”来使用发生错误的程序的下一行。
这是我的代码：
# LSTM [ Many to One ]
# START
# imports
import csv
import numpy as np
import tensorflow as tf
import sys
import os
import json
from random import shuffle
from tensorflow.python import debug as tf_debug
# CALCULATE ALL POSSIBLE BATCH SIZES
def calculate_batch_sizes(n_train):
batch_sizes = []
for i in range(2, int(n_train/2)):
if n_train % i == 0 and n_train / i > 1:
batch_sizes.append(i)
return batch_sizes
class lstm_network():
name = "lstm_"
# initialization function
def __init__(self, config_params):
self.sequence_length = config_params["sequence_length"]
self.batch_size = config_params["batch_size"]
self.hidden_layers_size = config_params["hidden_layers_size"]
self.data_path = config_params["data_path"]
self.n_epochs = config_params["no_of_epochs"]
self.learning_rate = config_params["learning_rate"]
self.w_igate, self.w_fgate, self.w_ogate, self.w_cgate = tf.get_variable('w_igate', shape = [self.sequence_length, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer()), tf.get_variable('w_fgate', shape = [self.sequence_length, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer()), tf.get_variable('w_ogate', shape = [self.sequence_length, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer()), tf.get_variable('w_cgate', shape = [self.sequence_length, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer())
self.u_igate, self.u_fgate, self.u_ogate, self.u_cgate = tf.get_variable('u_igate', shape = [self.hidden_layers_size, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer()), tf.get_variable('u_fgate', shape = [self.hidden_layers_size, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer()), tf.get_variable('u_ogate', shape = [self.hidden_layers_size, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer()), tf.get_variable('u_cgate', shape = [self.hidden_layers_size, self.hidden_layers_size], initializer = tf.contrib.layers.xavier_initializer())
self.outputs = [0.0] * self.batch_size
self.testing_loss = float(0)
self.training_loss = float(0)
self.ft, self.ct, self._ct, self.it = [0.0]*(self.hidden_layers_size), [0.0]*(self.hidden_layers_size), [0.0]*(self.hidden_layers_size), [0.0]*(self.hidden_layers_size)
self.ot, self.ht, self.ct_prev, self.ht_prev = [0.0]*(self.hidden_layers_size), [0.0]*(self.hidden_layers_size), np.array([0.0]*(self.hidden_layers_size)).reshape(1, self.hidden_layers_size), np.array([0.0]*(self.hidden_layers_size)).reshape(1, self.hidden_layers_size)
self.w_output_layer = tf.get_variable('w_output_layer', shape = [self.hidden_layers_size, 1], initializer = tf.contrib.layers.xavier_initializer())
print("\n Object of class lstm_network initialized with the given configuration")
# print values function
def print_model_info(self):
print("\n\n\n\t\t MODEL INFORMATION\n\n")
print("\n Weights of the LSTM layer: ")
print("\n\n input Gate Weights: \n w: ", self.w_igate,"\n u: ", self.u_igate)
print("\n\n Forget Gate Weights: \n w: ", self.w_fgate,"\n u: ", self.u_fgate)
print("\n\n Context Gate Weights: \n w: ", self.w_cgate,"\n u: ", self.u_cgate)
print("\n\n Output Gate Weights: \n w: ", self.w_ogate,"\n u: ", self.u_ogate)
print("\n\n Average loss while training: ", self.training_loss)
print("\n\n Average loss while testing: ", self.testing_loss)
# loading function
def load_data(self):
with open(self.data_path, 'r') as data_file:
data_reader = csv.reader(data_file, delimiter = ',')
self.data = [float(row[1]) for row in data_reader]
self.data_max, self.data_min, self.n_data = float(max(self.data)), float(min(self.data)), len(self.data)
for i in range(len(self.data)):
self.data[i] = float( (self.data[i]-self.data_min)/(self.data_max-self.data_min) )
self.data_x = [ self.data[i:i+self.sequence_length] for i in range(self.n_data - self.sequence_length-1)]
self.data_y = [ self.data[i] for i in range(self.sequence_length+1, self.n_data)]
self.n_data = len(self.data_x)
temp = list(zip(self.data_x,self.data_y))
shuffle(temp)
test_size = 0.25
self.data_x, self.data_y = zip(*temp)
self.trainx, self.trainy, self.testx, self.testy = self.data_x[:-int(test_size*self.n_data)], self.data_y[:-int(test_size*self.n_data)], self.data_x[-int(test_size*self.n_data):], self.data_y[-int(test_size*self.n_data):]
self.n_train, self.n_test = len(self.trainx), len(self.testx)
batch_sizes = []
batch_sizes.extend(calculate_batch_sizes(self.n_train))
while self.batch_size not in batch_sizes:
print("\n batch size provided in the initial configuration cannot be used, please select one from the following batch sizes:\n",batch_sizes)
self.batch_size = int(input("\n enter a batch size: "))
self.n_train_batches = int( self.n_train/self.batch_size )
self.trainx, self.trainy, self.testx, self.testy = np.float32(self.trainx), np.float32(self.trainy), np.float32(self.testx), np.float32(self.testy)
self.trainx_batches, self.trainy_batches = self.trainx.reshape(self.n_train_batches, self.batch_size, self.sequence_length), self.trainy.reshape(self.n_train_batches,self.batch_size, 1)
print("\n data loaded succesfully")
# graph building and training function
def build_graph_train(self):
outputs = [0.0]*self.batch_size#tf.placeholder(tf.float32, shape = [1, self.batch_size])
x = tf.placeholder(tf.float32, shape = [self.n_train_batches, self.batch_size, self.sequence_length], name = 'x')
ht_prev = tf.placeholder(tf.float32, shape = [1, self.hidden_layers_size], name = 'ht_prev')
ct_prev = tf.placeholder(tf.float32, shape = [1, self.hidden_layers_size], name = 'ct_prev')
self.ht_prev = np.array([0.0]*(self.hidden_layers_size)).reshape(1, self.hidden_layers_size)
self.ct_prev = np.array([0.0]*(self.hidden_layers_size)).reshape(1, self.hidden_layers_size)
for i1 in range(self.n_train_batches):
for i2 in range(self.batch_size):
#self.ht_prev = [self.ht_prev[i:i+9] for i in range(0, self.hidden_layers_size, 9)]
self.ft = tf.sigmoid( tf.matmul([x[i1][i2]], self.w_fgate) + tf.matmul(ht_prev, self.u_fgate) )
self.it = tf.sigmoid( tf.matmul([x[i1][i2]], self.w_igate) + tf.matmul(ht_prev, self.u_igate) )
self.ot = tf.sigmoid( tf.matmul([x[i1][i2]], self.w_ogate) + tf.matmul(ht_prev, self.u_ogate) )
self._ct = tf.sigmoid( tf.matmul([x[i1][i2]], self.w_cgate) + tf.matmul(ht_prev, self.u_cgate) )
self.ct = tf.tanh(tf.multiply(self.ft, ct_prev) + tf.multiply(self.it, self._ct))
self.ht = tf.multiply(self.ot, self.ct)
ht_prev = self.ht
ct_prev = self.ct
outputs[i2] = tf.nn.relu( tf.matmul(self.ht, self.w_output_layer) )
loss = tf.reduce_mean(tf.square(tf.subtract(outputs, self.trainy_batches[i1])))
self.ht_prev = ht_prev
self.ct_prev = ct_prev
self.train_op = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(loss)
print("\n Graph built \n\n Now training begins...\n")
#training
i = 0
avg_loss = float(0)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
#sess = tf_debug.LocalCLIDebugWrapperSession(sess)
for ep in range(self.n_epochs):
import pdb
pdb.set_trace()
sess.run(self.train_op, feed_dict= { x: self.trainx_batches, ht_prev: np.float32([0]*(self.hidden_layers_size)).reshape(1, self.hidden_layers_size), ct_prev: np.array([0.0]*(self.hidden_layers_size)).reshape(1, self.hidden_layers_size).astype(float) })
if ep % 10 == 0:
i += 1
mse = loss.eval()
avg_loss = float(avg_loss + mse)
print("\n Epoch: ", ep, "\t Loss: ", mse)
avg_loss = float(avg_loss/i)
self.training_loss = avg_loss
print("\n Training Loss: ", avg_loss)
# Predict function
def predict(self):
x_test_row = tf.placeholder(tf.float32, shape = [1, self.sequence_length])
avg_loss = float(0)
input_row = []
output_row = [0.0]
predictions = []
# one forward pass
self.ft = tf.sigmoid( tf.matmul([x_test_row], self.w_fgate) + tf.matmul(self.ht_prev, self.u_fgate) )
self.it = tf.sigmoid( tf.matmul([x_test_row], self.w_igate) + tf.matmul(self.htprev, self.u_igate ) )
self.ot = tf.sigmoid( tf.matmul([x_test_row], self.w_ogate) + tf.matmul(self.ht_prev, self.u_ogate) )
self._ct = tf.sigmoid( tf.matmul([x_test_row], self.w_cgate) + tf.matmul(self.ht_prev, self.u_cgate) )
self.ct = yf.tanh(tf.multiply(self.ft, self.ct_prev) + tf.multiply(self.it, self._ct))
self.ht = tf.matmul(self.ot,self.ct)
pred_output = tf.nn.relu( tf.matmul(self.ht, self.w_output_layer) )
with tf.Session() as sess:
for i1 in range(self.n_test):
del input_row[:]
output_row[0] = float(self.testy[i1])
for i2 in range(self.sequence_length):
input_row.append(self.testx[i1][i2])
sess.run(pred_output, feed_dict = { x_test_row: np.array(input_row).reshape(1, self.sequence_length)})
predictions.append(pred_output)
avg_error += abs(pred_output - output_row)
avg_error = float( avg_error/i1 )
self.testing_loss = avg_error
print("\n testing Error: ", avg_error)
return np.array(predictions)
# save model function
def save_model(self):
f = open("model_info.txt", "w+")
f.write("\n\n\n\n\t\t MODEL INFORMATION\n\n")
f.write("\n\n Weights of the LSTM layer: ")
f.write("\n\n\n input Gate Weights: \n w: ", self.w_igate,"\n u: ", self.u_igate)
f.write("\n\n\n Forget Gate Weights: \n w: ", self.w_fgate,"\n u: ", self.u_fgate)
f.write("\n\n\n Context Gate Weights: \n w: ", self.w_cgate,"\n u: ", self.u_cgate)
f.write("\n\n\n Output Gate Weights: \n w: ", self.w_ogate,"\n u: ", self.u_ogate)
f.write("\n\n\n Average loss while training: ", self.training_loss)
f.write("\n\n\n Average loss while testing: ", self.testing_loss)
f.close()
print("\n\n model's information saved in model_info.txt and weights stored in model.json\n\n")
f = open("model.json", "w+")
model_dict = { 'w_output_layer': self.w_output_layer, 'w_igate': self.w_igate, 'u_igate': self.u_igate, 'w_fgate': self.w_fgate, 'u_fgate': self.u_fgate, 'w_cgate': self.w_cgate, 'u_cgate': self.u_cgate, 'w_ogate': self.w_ogate, 'u_ogate': self.u_gate }
f.write(str(model_dict))
f.close()
# main function()
def main():
# parameters of the network
config_params = dict()
config_params["sequence_length"] = 3
config_params["batch_size"] = 33
config_params["hidden_layers_size"] = 9
config_params["data_path"] = "C:\\Users\\Rohan Baisantry\\Desktop\\Python, ML, Dl, RL and AI\\pythonfiles\\LSTM\\data.csv"
config_params["no_of_epochs"] = 1500
config_params["learning_rate"] = 0.01
# object of class lstm_network
test_object = lstm_network(config_params)
test_object.load_data()
test_object.build_graph_train()
predictions = test_object.predict()
print("\n predictions are: \n", predictions)
test_object.save_model()
# run
main()
以下是我得到的错误：
(base) C:\Users\Rohan Baisantry\Desktop\Python, ML, Dl, RL and AI\pythonfiles\LS
TM>python lstm_scratch_1layer.py
C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\h5py\__init__.py:36: Future
Warning: Conversion of the second argument of issubdtype from `float` to `np.flo
ating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(f
loat).type`.
from ._conv import register_converters as _register_converters
Object of class lstm_network initialized with the given configuration
data loaded succesfully
Graph built
Now training begins...
2018-05-31 12:16:34.526432: I T:\src\github\tensorflow\tensorflow\core\platform\
cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow bi
nary was not compiled to use: AVX2
> c:\users\rohan baisantry\desktop\python, ml, dl, rl and ai\pythonfiles\lstm\ls
tm_scratch_1layer.py(120)build_graph_train()
-> sess.run(self.train_op, feed_dict= { x: self.trainx_batches, ht_prev: np.floa
t32([0]*(self.hidden_layers_size)).reshape(1, self.hidden_layers_size), ct_prev:
np.array([0.0]*(self.hidden_layers_size)).reshape(1, self.hidden_layers_size).a
stype(float) })
(Pdb) n
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a va
lue for placeholder tensor 'ht_prev' with dtype float and shape [1,9]
[[Node: ht_prev = Placeholder[dtype=DT_FLOAT, shape=[1,9], _device="/jo
b:localhost/replica:0/task:0/device:CPU:0"]()]]
Caused by op 'ht_prev', defined at:
File "lstm_scratch_1layer.py", line 200, in <module>
main()
File "lstm_scratch_1layer.py", line 194, in main
test_object.build_graph_train()
File "lstm_scratch_1layer.py", line 88, in build_graph_train
ht_prev = tf.placeholder(tf.float32, shape = [1, self.hidden_layers_size], n
ame = 'ht_prev')
File "C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\tensorflow\python\o
ps\array_ops.py", line 1808, in placeholder
return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
File "C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\tensorflow\python\o
ps\gen_array_ops.py", line 5835, in placeholder
"Placeholder", dtype=dtype, shape=shape, name=name)
File "C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\tensorflow\python\f
ramework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\tensorflow\python\f
ramework\ops.py", line 3392, in create_op
op_def=op_def)
File "C:\Users\Rohan Baisantry\Anaconda3\lib\site-packages\tensorflow\python\f
ramework\ops.py", line 1718, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-
access
InvalidArgumentError (see above for traceback): You must feed a value for placeh
older tensor 'ht_prev' with dtype float and shape [1,9]
[[Node: ht_prev = Placeholder[dtype=DT_FLOAT, shape=[1,9], _device="/jo
b:localhost/replica:0/task:0/device:CPU:0"]()]]
现在即使我明确地提供了一个shape = [1，self.hidden_layers_size]和dtype = float32的数组，它仍然说我必须为ht_prev提供一个值。
答案 0 :(得分：0)
我认为你在定义self.ht_prev时犯了一个错误。
您有self.ht_prev = np.array([0.0]*(self.hidden_layers_size)).reshape(1, self.hidden_layers_size)这是我们期望被提供给会话的内容。但是，稍后在同一代码块中，您将其重新定义为self.ht_prev = ht_prev。而ht_prev原来是 Tensor ，而不是Numpy数组。
您的错误发生是因为您尝试将张量提供给feed_dict。