这是我的代码如下：
'''
Tensorflow LSTM classification of 16x30 images.
'''
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.ops import rnn, rnn_cell
import numpy as np
from numpy import genfromtxt
from sklearn.cross_validation import train_test_split
import pandas as pd
'''
a Tensorflow LSTM that will sequentially input several lines from each single image
i.e. The Tensorflow graph will take a flat (1,480) features image as it was done in Multi-layer
perceptron MNIST Tensorflow tutorial, but then reshape it in a sequential manner with 16 features each and 30 time_steps.
'''
blaine = genfromtxt('./Desktop/Blaine_CSV_lstm.csv',delimiter=',') # CSV transform to array
target = [row[0] for row in blaine] # 1st column in CSV as the targets
data = blaine[:, 1:481] #flat feature vectors
X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.05, random_state=42)
f=open('cs-training.csv','w') #1st split for training
for i,j in enumerate(X_train):
k=np.append(np.array(y_train[i]),j )
f.write(",".join([str(s) for s in k]) + '\n')
f.close()
f=open('cs-testing.csv','w') #2nd split for test
for i,j in enumerate(X_test):
k=np.append(np.array(y_test[i]),j )
f.write(",".join([str(s) for s in k]) + '\n')
f.close()
new_data = genfromtxt('cs-training.csv',delimiter=',') # Training data
new_test_data = genfromtxt('cs-testing.csv',delimiter=',') # Test data
x_train=np.array([ i[1::] for i in new_data])
ss = pd.Series(y_train) #indexing series needed for later Pandas Dummies one-hot vectors
y_train_onehot = pd.get_dummies(ss)
x_test=np.array([ i[1::] for i in new_test_data])
gg = pd.Series(y_test)
y_test_onehot = pd.get_dummies(gg)
# General Parameters
learning_rate = 0.001
training_iters = 100000
batch_size = 33
display_step = 10
# Tensorflow LSTM Network Parameters
n_input = 16 # MNIST data input (img shape: 28*28)
n_steps = 30 # timesteps
n_hidden = 128 # hidden layer num of features
n_classes = 20 # MNIST total classes (0-9 digits)
# tf Graph input
x = tf.placeholder("float", [None, n_steps, n_input])
y = tf.placeholder("float", [None, n_classes])
# Define weights
weights = {
'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))
}
biases = {
'out': tf.Variable(tf.random_normal([n_classes]))
}
def RNN(x, weights, biases):
# Prepare data shape to match `rnn` function requirements
# Current data input shape: (batch_size, n_steps, n_input)
# Required shape: 'n_steps' tensors list of shape (batch_size, n_input)
# Permuting batch_size and n_steps
x = tf.transpose(x, [1, 0, 2])
# Reshaping to (n_steps*batch_size, n_input)
x = tf.reshape(x, [-1, n_input])
# Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)
x = tf.split(0, n_steps, x)
# Define a lstm cell with tensorflow
with tf.variable_scope('cell_def'):
lstm_cell = tf.nn.rnn_cell.LSTMCell(n_hidden, forget_bias=1.0)
# Get lstm cell output
with tf.variable_scope('rnn_def'):
outputs, states = tf.nn.rnn(lstm_cell, x, dtype=tf.float32)
# Linear activation, using rnn inner loop last output
return tf.matmul(outputs[-1], weights['out']) + biases['out']
pred = RNN(x, weights, biases)
# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Evaluate model
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
step = 1
# Keep training until reach max iterations
while step * batch_size < training_iters:
batch_x = np.split(x_train, 15)
batch_y = np.split(y_train_onehot, 15)
for index in range(len(batch_x)):
ouh1 = batch_x[index]
ouh2 = batch_y[index]
# Reshape data to get 28 seq of 28 elements
ouh1 = np.reshape(ouh1,(batch_size, n_steps, n_input))
sess.run(optimizer, feed_dict={x: ouh1, y: ouh2}) # Run optimization op (backprop)
if step % display_step == 0:
# Calculate batch accuracy
acc = sess.run(accuracy, feed_dict={x: ouh1, y: ouh2})
# Calculate batch loss
loss = sess.run(cost, feed_dict={x: ouh1, y: ouh2})
print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
"{:.6f}".format(loss) + ", Training Accuracy= " + \
"{:.5f}".format(acc))
step += 1
print("Optimization Finished!")
我收到以下错误，似乎我在第 92 行和第 97 行重新迭代相同的变量，并且我担心这可能是与 RNN def 端的 Tensorflow 0.10.0 不兼容的情况：
ValueError: Variable RNN/BasicLSTMCell/Linear/Matrix already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:
File "/home/mohsen/lstm_mnist.py", line 92, in RNN
outputs, states = tf.nn.rnn(lstm_cell, x, dtype=tf.float32)
File "/home/mohsen/lstm_mnist.py", line 97, in <module>
pred = RNN(x, weights, biases)
File "/home/mohsen/anaconda2/lib/python2.7/site-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 81, in execfile
builtins.execfile(filename, *where)
此错误的根源是什么？我该如何解决它？
编辑：从我在代码上构建的原始存储库中，相同的variable_scope问题仍然存在https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py
