Python 如何在函数中将张量类型转换为numpy-narray类型？

在我的代码中，我实现了一个名为my_conv_函数的函数来替换tf.nn.conv2d函数。我的函数需要numpy.narray类型参数，但x和y在tensorflow中都是张量类型。如何将它们转换为numpy.narray类型？

sess.run[yourTensor]或yourTensor.eval应返回所需的numpy数组。我可能错了，但我的印象是，做太多次会减慢速度，因为基本上每次都要运行图表

你试过数组=yourTensor.evalsession=yourSession吗？def conv2dx，w:printw.evalsession=sess result=tf.nn.conv2dx，w，strips=[1,1,1]，padding='SAME'返回结果[1,1,1]，这行得通吗？///你有什么建议来实现我的想法吗？我想用python编写的函数替换tensorflow函数，但我还需要tensorflow函数来处理函数的结果。我怎样才能让他们一起工作呢？我是tensorflow的新用户。正确的方法是使用张量。如果你必须动态计算张量，它最终会变慢。谢谢你的建议！也许我应该这样做。

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x, w):
    x_narray = something operate on x
    w_narray = something operate on w
    result = my_conv_function(x_narray, w_narray, strides=[1, 1, 1, 1], padding='SAME')
    return result

def max_pool_2_2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],    padding='SAME')


mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])
x_image = tf.reshape(x, [-1, 28, 28, 1])

w_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1)
h_pool1 = max_pool_2_2(h_conv1)

w_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2)
h_pool2 = max_pool_2_2(h_conv2)

w_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)

keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

w_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2) + b_fc2)

cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

sess = tf.Session()
with sess.as_default():
    sess.run(tf.initialize_all_variables())
    for i in range(10000):
        batch = mnist.train.next_batch(50)
        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
            print("step %d, train_accuracy %g" % (i, train_accuracy))
            train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
    print("test accuracy %g" % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))