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[英]Getting an error when trying to return the values of multiple metrics from mode.evaluate of Keras neural Network
[英]Error when trying to split neural network testing
如果我有tensorflow神经网络,则可以像这样在测试数据上运行:
result = sess.run(y_conv, feed_dict={x: test_inputs})
但是,这可能会遇到内存问题,因此我尝试按以下方式分解计算:
result = []
for i in range(0, len(test_inputs), 100):
end = min(i+100 - 1, len(test_inputs) - 1)
r = sess.run(y_conv, feed_dict={x: test_inputs.loc[i:end, :]})
result.append(r)
但是,现在出现此错误:
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_2' with dtype float
[[Node: Placeholder_2 = Placeholder[dtype=DT_FLOAT, shape=<unknown>, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
那么,这个问题的原因是什么呢? 我本以为网络可以在较小的示例批次上很好地工作。
如果没有相关性,则将按如下方式创建神经网络:
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1, 28, 28, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(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_2x2(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(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
您已输入x
作为输入,但未输入keep_prob
。 您的网络看起来类似于Deep MNIST for Experts 。 示例片段:
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
为了进行推断,您应该将keep_prob更改为1.0。
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