Question

HeJ小鼠，

在我的IRIS数据集的多类神经网络的最后一步中，我正在执行以下代码：

steps = 2500

with tf.Session() as sess:

sess.run(init)

for i in range(steps):

    sess.run(train,feed_dict={X_data:X_train,y_target:y_train})

    # PRINT OUT A MESSAGE EVERY 100 STEPS
    if i%500 == 0:

        print('Currently on step {}'.format(i))
        print('Accuracy is:')
        # Test the Train Model
        matches = tf.equal(tf.argmax(final_output,1),tf.argmax(y_target,1))

        acc = tf.reduce_mean(tf.cast(matches,tf.float32))

        print(sess.run(acc,feed_dict={X_data:X_test,y_target:y_test}))
        print('\n')

correct_prediction = tf.equal(tf.argmax(final_output,1), tf.argmax(y_target,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print("Final accuracy: ", sess.run(accuracy, feed_dict={X_data: X_test, y_target: y_test}))

我在这里的最后一步是预测手动输入值的输出。我试过这个：

prediction=tf.argmax(final_output,1)
print("Predictions")

new = [5.1,3.5,1.4,0.2]

print(prediction.eval(feed_dict={X_data: new}))

但是我收到以下错误

Cannot feed value of shape (4,) for Tensor 'Placeholder_10:0', which has shape '(?, 4)'

我真的不知道如何创建一个包含4个手动输入值的列表，这些值符合此占位符的格式

X_data = tf.placeholder(shape=[None, 4], dtype=tf.float32)

谢谢！

Answer 1

只需在列表中包含 new 即可：

assert len(x.shape) == 2, "input must be 2D"
assert x.shape[1] == 16, "input must have 16 columns"
assert np.issubdtype(x.dtype, np.integer), "input must be integers"

或者喂一个numpy数组：

prediction.eval(feed_dict={X_data: [new]})

无法为Tensor'Plankholder_10：0'提供形状值（4，），其形状为'（？，4）'

1 个答案: