Sufeng Niu - 1 year ago 296

Python Question

I use a tensorflow to implement a simple multi-layer perceptron for regression. The code is modified from standard mnist classifier, that I only changed the output cost to MSE (use

`tf.reduce_mean(tf.square(pred-y))`

`target: 48.129, estimated: 42.634`

target: 46.590, estimated: 42.634

target: 34.209, estimated: 42.634

target: 69.677, estimated: 42.634

......

I have tried different batch size, different initialization, input normalization using sklearn.preprocessing.scale (my inputs range are quite different). However, none of them worked. I have also tried one of sklearn example from Tensorflow (Deep Neural Network Regression with Boston Data). But I got another error in line 40:

'module' object has no attribute 'infer_real_valued_columns_from_input'

Anyone has clues on where the problem is? Thank you

My code is listed below, may be a little bit long, but very straghtforward:

`from __future__ import absolute_import`

from __future__ import division

from __future__ import print_function

import tensorflow as tf

from tensorflow.contrib import learn

import matplotlib.pyplot as plt

from sklearn.pipeline import Pipeline

from sklearn import datasets, linear_model

from sklearn import cross_validation

import numpy as np

boston = learn.datasets.load_dataset('boston')

x, y = boston.data, boston.target

X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(

x, y, test_size=0.2, random_state=42)

total_len = X_train.shape[0]

# Parameters

learning_rate = 0.001

training_epochs = 500

batch_size = 10

display_step = 1

dropout_rate = 0.9

# Network Parameters

n_hidden_1 = 32 # 1st layer number of features

n_hidden_2 = 200 # 2nd layer number of features

n_hidden_3 = 200

n_hidden_4 = 256

n_input = X_train.shape[1]

n_classes = 1

# tf Graph input

x = tf.placeholder("float", [None, 13])

y = tf.placeholder("float", [None])

# Create model

def multilayer_perceptron(x, weights, biases):

# Hidden layer with RELU activation

layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])

layer_1 = tf.nn.relu(layer_1)

# Hidden layer with RELU activation

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

layer_2 = tf.nn.relu(layer_2)

# Hidden layer with RELU activation

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

layer_3 = tf.nn.relu(layer_3)

# Hidden layer with RELU activation

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

layer_4 = tf.nn.relu(layer_4)

# Output layer with linear activation

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

return out_layer

# 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))

}

# Construct model

pred = multilayer_perceptron(x, weights, biases)

# Define loss and optimizer

cost = tf.reduce_mean(tf.square(pred-y))

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# Launch the graph

with tf.Session() as sess:

sess.run(tf.initialize_all_variables())

# Training cycle

for epoch in range(training_epochs):

avg_cost = 0.

total_batch = int(total_len/batch_size)

# Loop over all batches

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]

# Run optimization op (backprop) and cost op (to get 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

# sample prediction

label_value = batch_y

estimate = p

err = label_value-estimate

print ("num batch:", total_batch)

# Display logs per epoch step

if epoch % display_step == 0:

print ("Epoch:", '%04d' % (epoch+1), "cost=", \

"{:.9f}".format(avg_cost))

print ("[*]----------------------------")

for i in xrange(3):

print ("label value:", label_value[i], \

"estimated value:", estimate[i])

print ("[*]============================")

print ("Optimization Finished!")

# Test model

correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))

# Calculate accuracy

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

print ("Accuracy:", accuracy.eval({x: X_test, y: Y_test}))

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Answer Source

**Short answer**:

Transpose your `pred`

vector using `tf.transpose(pred)`

.

**Longer answer**:

The problem is that `pred`

(the predictions) and `y`

(the labels) are not of the same shape: one is a row vector and the other a column vector. Apparently when you apply an element-wise operation on them, you'll get a matrix, which is not what you want.

The solution is to transpose the prediction vector using `tf.transpose()`

to get a proper vector and thus a proper loss function. Actually, if you set the batch size to 1 in your example you'll see that it works even without the fix, because transposing a 1x1 vector is a no-op.

I applied this fix to your example code and observed the following behaviour. Before the fix:

```
Epoch: 0245 cost= 84.743440580
[*]----------------------------
label value: 23 estimated value: [ 27.47437096]
label value: 50 estimated value: [ 24.71126747]
label value: 22 estimated value: [ 23.87785912]
```

And after the fix at the same point in time:

```
Epoch: 0245 cost= 4.181439120
[*]----------------------------
label value: 23 estimated value: [ 21.64333534]
label value: 50 estimated value: [ 48.76105118]
label value: 22 estimated value: [ 24.27996063]
```

You'll see that the cost is much lower and that it actually learned the value 50 properly. You'll have to do some fine-tuning on the learning rate and such to improve your results of course.

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