我想運行下面的tensorflow代碼,它在第一次運行正常。如果我嘗試再次運行它,它不斷拋出一個錯誤說第二次運行tensorflow時出錯
ValueError: Variable layer1/weights1 already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:
File "C:\Users\owner\Anaconda3\envs\DeepLearning_NoGPU\lib\site-packages\tensorflow\python\framework\ops.py", line 1228, in __init__
self._traceback = _extract_stack()
File "C:\Users\owner\Anaconda3\envs\DeepLearning_NoGPU\lib\site-packages\tensorflow\python\framework\ops.py", line 2336, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Users\owner\Anaconda3\envs\DeepLearning_NoGPU\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 768, in apply_op
op_def=op_def)
如果我重新啓動控制檯,然後運行它,再一次它運行得很好。
以下給出了我對神經網絡的實現。
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
import tensorflow as tf
learning_rate = 0.001
training_epochs = 100
n_input = 9
n_output = 1
n_layer1_node = 100
n_layer2_node = 100
X_train = np.random.rand(100, 9)
y_train = np.random.rand(100, 1)
with tf.variable_scope('input'):
X = tf.placeholder(tf.float32, shape=(None, n_input))
with tf.variable_scope('output'):
y = tf.placeholder(tf.float32, shape=(None, 1))
#layer 1
with tf.variable_scope('layer1'):
weight_matrix1 = {'weights': tf.get_variable(name='weights1',
shape=[n_input, n_layer1_node],
initializer=tf.contrib.layers.xavier_initializer()),
'biases': tf.get_variable(name='biases1',
shape=[n_layer1_node],
initializer=tf.zeros_initializer())}
layer1_output = tf.nn.relu(tf.add(tf.matmul(X, weight_matrix1['weights']), weight_matrix1['biases']))
#Layer 2
with tf.variable_scope('layer2'):
weight_matrix2 = {'weights': tf.get_variable(name='weights2',
shape=[n_layer1_node, n_layer2_node],
initializer=tf.contrib.layers.xavier_initializer()),
'biases': tf.get_variable(name='biases2',
shape=[n_layer2_node],
initializer=tf.zeros_initializer())}
layer2_output = tf.nn.relu(tf.add(tf.matmul(layer1_output, weight_matrix2['weights']), weight_matrix2['biases']))
#Output layer
with tf.variable_scope('layer3'):
weight_matrix3 = {'weights': tf.get_variable(name='weights3',
shape=[n_layer2_node, n_output],
initializer=tf.contrib.layers.xavier_initializer()),
'biases': tf.get_variable(name='biases3',
shape=[n_output],
initializer=tf.zeros_initializer())}
prediction = tf.nn.relu(tf.add(tf.matmul(layer2_output, weight_matrix3['weights']), weight_matrix3['biases']))
cost = tf.reduce_mean(tf.squared_difference(prediction, y))
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
with tf.Session() as session:
session.run(tf.global_variables_initializer())
for epoch in range(training_epochs):
session.run(optimizer, feed_dict={X: X_train, y: y_train})
train_cost = session.run(cost, feed_dict={X: X_train, y:y_train})
print(epoch, " epoch(s) done")
print("training complete")
由於錯誤表明我嘗試添加reuse=True
爲with tf.variable_scope():
參數但再次無法正常工作。
我在conda環境中運行這個。我在Windows 10中使用Python 3.5和CUDA 8(但它應該沒有關係,因爲它沒有配置爲在GPU中運行)。