Question

我有一个多层感知器用于多输出回归问题，可以预测14个连续值。以下是相同的代码片段：

# Parameters
learning_rate = 0.001
training_epochs = 1000
batch_size = 500

# Network Parameters
n_hidden_1 = 32 
n_hidden_2 = 200 
n_hidden_3 = 200
n_hidden_4 = 256
n_input = 14
n_classes = 14

# tf Graph input
x = tf.placeholder("float", [None, n_input],name="x")
y = tf.placeholder("float", [None, n_classes])

# Store layers weight & bias
weights = {
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1], 0, 0.1)),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], 0, 0.1)),
    'h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3], 0, 0.1)),
    'h4': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_4], 0, 0.1)),
    'out': tf.Variable(tf.random_normal([n_hidden_4, n_classes], 0, 0.1))
}

biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1], 0, 0.1)),
    'b2': tf.Variable(tf.random_normal([n_hidden_2], 0, 0.1)),
    'b3': tf.Variable(tf.random_normal([n_hidden_3], 0, 0.1)),
    'b4': tf.Variable(tf.random_normal([n_hidden_4], 0, 0.1)),
    'out': tf.Variable(tf.random_normal([n_classes], 0, 0.1))
}

# Create model
def multilayer_perceptron(x):
    # Hidden layer with RELU activation
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)

    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    layer_2 = tf.nn.relu(layer_2)

    layer_3 = tf.add(tf.matmul(layer_2, weights['h3']), biases['b3'])
    layer_3 = tf.nn.relu(layer_3)

    layer_4 = tf.add(tf.matmul(layer_3, weights['h4']), biases['b4'])
    layer_4 = tf.nn.relu(layer_4)

    out_layer = tf.matmul(layer_4, weights['out']) + biases['out']
    return out_layer   

# Construct model
pred = multilayer_perceptron(x)
cost = tf.reduce_mean(tf.square(pred-y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# Run the graph in the session 
init = tf.global_variables_initializer()
with tf.Session() as sess:
      sess.run(init)
      for epoch in range(training_epochs):
            avg_cost = 0.
            total_batch = int(total_len/batch_size)
            for i in range(total_batch-1):
                batch_x = X_train[i*batch_size:(i+1)*batch_size]
                batch_y = Y_train[i*batch_size:(i+1)*batch_size]
                _, c, p = sess.run([optimizer, cost, pred], feed_dict={x: batch_x, y: batch_y})
                avg_cost += c / total_batch

输出：

x_batch_data:
[  1.77560000e+04   4.00000000e+00   4.00000000e+00 ...,   1.00000000e+00
5.61000000e+02   1.00000000e+00]
[  1.34310000e+04   4.00000000e+00   4.00000000e+00 ...,   1.00000000e+00
5.61000000e+02   1.00000000e+00]
[  2.98800000e+03   1.00000000e+00   0.00000000e+00 ...,   0.00000000e+00
0.00000000e+00   1.00000000e+00]

y_batch_data:
[[  4.19700000e-01   1.04298450e+02   1.50000000e+02 ...,   2.75250000e-01
1.02000000e-01   7.28565000e+00]
[  5.59600000e-01   1.39064600e+02   2.00000000e+02 ...,   3.67000000e-01
1.36000000e-01   9.71420000e+00]
[  2.79800000e-01   6.95323000e+01   1.00000000e+02 ...,   1.83500000e-01
6.80000000e-02   4.85710000e+00]

Prediction: 
[[   0.85085869   90.53585815  130.17015076 ...,    0.62335277
 0.26637274    5.52062225]
[   0.85085869   90.53585815  130.17015076 ...,    0.62335277
 0.26637274    5.52062225]
[   0.85085869   90.53585815  130.17015076 ...,    0.62335277
 0.26637274    5.52062225]

尽管输入值不同，但预测值始终相同。有人可以指出这可能是背后的原因吗？

P.S提到的类似问题：tensorflow deep neural network for regression always predict same results in one batch

尝试的方法：
1.逐步将学习率从0.1降低到0.0001
2.尝试其他优化算法
3.更改了网络架构（隐藏节点和层的数量以及激活功能）

感谢任何帮助。

Answer 1

问题似乎是：

你没有调用tf.global_variables_initializer（）。run（）
批次不是随机选择的（INDEXES永远不会被引用），因此您每次都可能正在进行相同的批次
并且权重永远不会改变（你没有在sess.run（）中获取＆＃39;优化器＆＃39;）

如果权重保持不变且批次输入保持不变，则预测将保持不变。希望这有助于解决它。

多输出回归模型始终在Tensorflow中为批次返回相同的值

1 个答案: