使用TensorFlow算法调试回归-python

Question

早上，我在TensorFlow方面取得了一些进展，但它仍然很困难。我发现很多在线示例没有运行。

无论如何，我试着编写一些代码来应用多层神经网络来解决回归问题。但是，我只能得到零。有人能够帮助我找到我的代码和理解中哪里出错吗？

我在Windows 10上运行Tensorflow 0.12与Python 3.5

非常感谢

import tensorflow as tf 
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 

# Parameters 
learning_rate = 0.001 
training_epochs = 100 
batch_size = 5000 

# Network Parameters 
n_hidden_1 = 256 
n_hidden_2 = 10 
n_input = 9 
n_classes = 1 
n_samples = dataVar.shape[0] 

# TensorFlow Graph Input 
x = tf.placeholder("float", [None, n_input]) 
if (n_classes > 1): 
    y = tf.placeholder("float", [None, n_classes]) 
else: 
    y = tf.placeholder("float", [None,]) 

# Create Multilayer Model 
def multilayer_perceptron(x, weights, biases): 
    ''' 
    x: Place holder for data input 
    weights: Dictionary of weights 
    biases: Dictionary of biases 
    ''' 

    # First hidden layer with RELU activation 
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) 
    layer_1 = tf.nn.relu(layer_1) 

    # Second hidden layer with RELU activation 
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) 
    layer_2 = tf.nn.relu(layer_2) 

    # Last output layer with linear activation 
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] 
    return out_layer 

# weights and biases 
weights = { 
     'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), 
     'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), 
     'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes])) 
} 

biases = { 
     'b1' : tf.Variable(tf.random_normal([n_hidden_1])), 
     'b2': tf.Variable(tf.random_normal([n_hidden_2])), 
     'out': tf.Variable(tf.random_normal([n_classes])) 
} 

# Construct Model 
pred = multilayer_perceptron(x, weights, biases) 


# Define loss and optimizer 
cost = tf.reduce_mean(tf.square(pred - y)) 
#cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) 
optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(cost) 

# Initialize variables 
init = tf.initialize_all_variables() 

# RUNNING THE SESSION 

# launch the session 
sess = tf.InteractiveSession() 


# Initialize all the variables 
sess.run(init) 

# Training Epochs 
for epoch in range(training_epochs): 
    # Start with cost = 0 
    avg_cost = 0.0 

    # Convert total number of batches to integer 
    total_batch = int(n_samples/batch_size) 

    # Loop over all batches 
    for i in range(total_batch): 
     # Grab the next batch of training data and labels 
     ind = np.random.randint(0, high=dataVar_scaled.shape[0], size=(batch_size)) 
     batch_x = dataVar_scaled[ind,:] 
     batch_y = depth[ind] 

     # Feed dictionary for optimization and loss value 
     _, c,p = sess.run([optimizer, cost,pred], feed_dict={x: batch_x, y: batch_y}) 

     # Compute average loss 
     avg_cost += c/total_batch 

    print("Epoch: {} cost = {:.4f}".format(epoch+1, avg_cost)) 

print("Model has completed {} Epochs of training".format(training_epochs)) 


prediction = tf.argmax(sess.run(pred, feed_dict={x:dataVar_scaled}),1).eval() 
print(prediction) 
plt.plot(prediction,depth,'b.') 

Answer 1

我不知道通过回归，您的意思是否线性回归或逻辑回归。但看着你的代码，我相信你的意思是线性回归。

在情况下，它是线性回归，您的问题是在这行代码：

prediction = tf.argmax(sess.run(pred, feed_dict={x:dataVar_scaled}),1).eval()

您正在最大值的位置（使用tf.argmax()）存在于您的代码值。由于n_classes = 1，您的输出中只存在一个类，它总是首先出现，这就是为什么您获得零值。 因此，将该行更改为

prediction = sess.run(pred, feed_dict={x:dataVar_scaled})。

现在您将获得输出的值而不是最大值的位置。

如果是逻辑回归，你显然只有一个类。如果只将输出作为一个班级，你怎么能做分类。

希望这可以帮助你。