TensorFlow学习之路(0):tf.placeholder() & feed_dict={}

本文介绍了如何在TensorFlow中使用feed_dict为tensor赋值,包括通过Session.run(), Tensor.eval() 或 Operation.run()等方法来实现这一过程。示例代码展示了如何对两个tensor进行矩阵相乘操作。

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使用feed_dict={}为tensor赋值,通过Session.run() or Tensor.run() or Operation.run()来实现


x = tf.placeholder(tf.float32, shape=[1024, 1024])    # x是一个tensor

y = tf.matual(x, x)                                                      #y是一个tensor

z = np.random.rand(1024, 1024)


sess.run(y, feed_dict={x: z})

y.eval(feed_dict={x: z})

y.run(feed_dict={x: z})                                          # if y is an operation


对下面代码进行改错 import tensorflow.compat.v1 as tf tf.compat.v1.disable_eager_execution() from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('MNIST_data', one_hot=True) num_classes = 10 input_size = 784 hidden_units_size = 30 batch_size = 100 training_iterations = 10000 X = tf.placeholder(tf.float32, [None, input_size]) Y = tf.placeholder(tf.float32, [None, num_classes]) W1 = tf.Variable(tf.random_normal([input_size, hidden_units_size],stddev = 0.1)) B1 = tf.Variable(tf.constant([hidden_units_size])) W2 = tf.Variable(tf.random_normal ([hidden_units_size,num_classes],stddev = 0.1)) B2 = tf.Variable(tf.constant(0.1), [num_classes]) hidden_opt = tf.matmul(X, W1) + B1 hidden_opt = tf.nn.relu(hidden_opt) final_opt = tf.matmul(hidden_opt, W2) + B2 final_opt = tf.nn.relu(final_opt) loss1 = tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=final_opt) loss = tf.reduce_mean(loss1) opt = tf.train.GradientDescentOptimizer(0.05).minimize(loss) init = tf.global_variables_initializer() correct_prediction = tf.equal(tf.argmax(Y,1), tf.argmax(final_opt,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float')) sess = tf.Session() sess.run(init) for i in range(training_iterations): batch = mnist.train.next_batch(batch_size) batch_input = batch[0] batch_labels = batch[1] train_loss = sess.run([opt, loss], feed_dict={X: batch_input, Y: batch_labels}) if i % 100 == 0: train_accuracy = accuracy.eval (session = sess, feed_dict={X: batch_input, Y: batch_labels}) print("step %d, training accuracy %g" % (i, train_accuracy))
最新发布
04-01
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