Question

我正在尝试在Jupyter Notebook上使用tensorflow和gpu实现一个简单的神经网络，但每次创建会话都失败了，我已经多次跟踪代码并将迭代次数减少到10次，并且还减少了输入tain和测试数据，仅用于测试它是否是计算能力的问题。我的网络有2个隐藏层;第一个包含3个神经元，第二个包含2个神经元。这是我的代码：

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import math
import tensorflow as tf
from tensorflow.python.framework import ops
from preprocessing import load_dataset



def model(X_train, Y_train, X_test, Y_test, learning_rate=0.0001):
"""
Arguments:
X_train -- training set, of shape (input size , number of training examples )
Y_train -- test set, of shape (output size , number of training examples )
X_test -- training set, of shape (input size , number of test examples )
Y_test -- test set, of shape (output size, number of test examples )
learning_rate -- learning rate of the optimization

Returns:
parameters -- parameters learnt by the model.
"""

ops.reset_default_graph()  
X, Y = create_placeholders()
parameters = initialize_parameters()
Z3 = forward_propagation(X, parameters)
cost = compute_cost(Z3, Y)
print("X = " + str(X))
print("Y = " + str(Y))
print("Z3 = " + str(Z3))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)

init = tf.global_variables_initializer()

with tf.Session() as sess:

    sess.run(init)

    for i in range(10):
        print(sess.run([optimizer, cost], feed_dict={X: X_train, Y: Y_train}))

    parameters = sess.run(parameters)
    print("Parameters have been trained!")

    correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))

    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

    print("Train Accuracy:", accuracy.eval({X: X_train, Y: Y_train}))
    print("Test Accuracy:", accuracy.eval({X: X_test, Y: Y_test}))

    return parameters

X_train, Y_train, X_test, Y_test=load_dataset()
parameters= model(X_train, Y_train, X_test, Y_test)

以下是我正在调用的函数

 def create_placeholders():
    X = tf.placeholder(dtype=tf.float32, shape=(28755648, 5), name="X")
    Y = tf.placeholder(dtype=tf.float32, shape=(1, 5), name="Y")

    return X, Y
    def initialize_parameters():
    W1 = tf.get_variable("W1", [3, 28755648], initializer=tf.contrib.layers.xavier_initializer(seed=0))
    b1 = tf.get_variable("b1", [3, 1], initializer=tf.zeros_initializer())
    W2 = tf.get_variable("W2", [2, 3], initializer=tf.contrib.layers.xavier_initializer(seed=0))
    b2 = tf.get_variable("b2", [2, 1], initializer=tf.zeros_initializer())
    W3 = tf.get_variable("W3", [1, 2], initializer=tf.contrib.layers.xavier_initializer(seed=0))
    b3 = tf.get_variable("b3", [1, 1], initializer=tf.zeros_initializer())

    parameters = {"W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2,
                  "W3": W3,
                  "b3": b3}

    return parameters

def forward_propagation(X, parameters):
    W1 = parameters['W1']
    b1 = parameters['b1']
    W2 = parameters['W2']
    b2 = parameters['b2']
    W3 = parameters['W3']
    b3 = parameters['b3']


    Z1 = tf.add(tf.matmul(W1, X), b1)     
    A1 = tf.nn.relu(Z1)                   
    Z2 = tf.add(tf.matmul(W2, A1), b2)    
    A2 = tf.nn.relu(Z2)                   
    Z3 = tf.add(tf.matmul(W3, A2), b3)    

    return Z3

 def compute_cost(Z3, Y):
    logits = Z3
    labels = Y
    cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))


    return cost

每次运行代码时都会出现以下错误：

---------------------------------------------------------------------------
InternalError                             Traceback (most recent call last)
<ipython-input-42-9ce12ddb96d9> in <module>()
----> 1 parameters= model(X_train, Y_train, X_test, Y_test)

<ipython-input-38-ab5d84d97720> in model(X_train, Y_train, X_test, Y_test, learning_rate)
     42 
     43     # Start the session to compute the tensorflow graph
---> 44     with tf.Session() as sess:
     45 
     46         # Run the initialization

C:\Users\Chaymae\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in __init__(self, target, graph, config)
   1480 
   1481     """
-> 1482     super(Session, self).__init__(target, graph, config=config)
   1483     # NOTE(mrry): Create these on first `__enter__` to avoid a reference cycle.
   1484     self._default_graph_context_manager = None

C:\Users\Chaymae\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in __init__(self, target, graph, config)
    620           # pylint: enable=protected-access
    621         else:
--> 622           self._session = tf_session.TF_NewDeprecatedSession(opts, status)
    623     finally:
    624       tf_session.TF_DeleteSessionOptions(opts)

C:\Users\Chaymae\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
    471             None, None,
    472             compat.as_text(c_api.TF_Message(self.status.status)),
--> 473             c_api.TF_GetCode(self.status.status))
    474     # Delete the underlying status object from memory otherwise it stays alive
    475     # as there is a reference to status from this from the traceback due to

InternalError: Failed to create session.

另一方面，我尝试创建新会话并运行一个简单的张量流图，它运行良好。如果有人能帮助我，我会很高兴

Answer 1

在创建tf.reset_default_graph()之前设置tf.Session()应解决问题。

tensorflow内部错误无法创建会话

1 个答案: