Question

我正在尝试生成神经网络成本/损失函数的标量摘要。以下是我的代码的一部分。

...
cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=predictions, labels=y))
cost_summary = tf.summary.scalar(name='cost_summary', tensor=cost)
optimizer = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(cost)


with tf.Session() as sess:

    # Step 1. Initializing the session
    init = tf.global_variables_initializer()
    writer = tf.summary.FileWriter('/home/dileep/Desktop', sess.graph)

    sess.run(init)

    # Step 2. Dividing x and y to batches
    for epoch in range(training_epochs):
        avg_cost = 0.0
        total_batch = int(len(train_x)//batch_size)
        x_batches = np.array_split(train_x, total_batch)
        y_batches = np.array_split(train_y, total_batch)
        # Step 3. Run session, calculate cost.
        for i in range(total_batch):
            batch_x, batch_y = x_batches[i], y_batches[i]
            _, c = sess.run([optimizer, cost], feed_dict={
                                                    x:batch_x, 
                                                    y:batch_y, keep_prob:0.4})
            avg_cost += c/total_batch
        # Step 4. Print the outputs
        if epoch % display_step == 0:
            print("Epoch:", '%0d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))

    summary = sess.run(cost_summary)
    writer.add_summary(summary, epoch)
    print("Optimization finished!")

    correct_prediction = tf.equal(tf.argmax(predictions, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
    print('Accuracy:', accuracy.eval({x: test_x, y: test_y, keep_prob:0.8}))

此代码生成以下错误

InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [?,1]
     [[{{node Placeholder_1}}]]

During handling of the above exception, another exception occurred:

InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-9-ba759ef3de6c> in <module>
     21                                                     y:batch_y, keep_prob:0.4})
     22             avg_cost += c/total_batch
---> 23             summary = sess.run(cost_summary)
     24         # Step 4. Print the outputs
     25         if epoch % display_step == 0:

我不明白他们在谈论哪个占位符。谁能告诉我如何解决此错误。谢谢

标量摘要-张量流生成错误

0 个答案: