MLP in tensorflow for regression... not converging
<p>Hello it is my first time working with tensorflow, i try to adapt the example here <a href="https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/multilayer_perceptron.py" rel="nofollow">TensorFlow-Examples</a> to use this code for regression problems with boston database. Basically,  i only change the cost function ,the database, the inputs number, and the target number but when i run the MPL doesn't converge (i use a very low rate). I test it with Adam Optimization and descend gradient optimization but i have the same behavior.
I appreciate your suggestions and ideas...!!!</p>

<p><em>Observation: When i ran this program without the modifications described above, the cost function value always decrease.</em></p>

<p>Here the evolution when i run the model, the cost function oscillated even with a very low learning rate.In the worst case, i hope the model converge in a value, for example the epoch 944 shows a value 0.2267548 if not other better value is find then this value must stay until the optimization is finished.</p>

<p>Epoch: 0942 cost= 0.445707272</p>

<p>Epoch: 0943 cost= 0.389314095</p>

<p>Epoch: 0944 cost= 0.226754842</p>

<p>Epoch: 0945 cost= 0.404150135</p>

<p>Epoch: 0946 cost= 0.382190095</p>

<p>Epoch: 0947 cost= 0.897880572</p>

<p>Epoch: 0948 cost= 0.481954243</p>

<p>Epoch: 0949 cost= 0.269408980</p>

<p>Epoch: 0950 cost= 0.427961614</p>

<p>Epoch: 0951 cost= 1.206053280</p>

<p>Epoch: 0952 cost= 0.834200084</p>

<pre><code>from __future__ import print_function

# Import MNIST data
#from tensorflow.examples.tutorials.mnist import input_data
#mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

import tensorflow as tf
import  ToolInputData as input_data

ALL_DATA_FILE_NAME = "boston_normalized.csv"



##Load complete database, then this database is splitted in training,   validation and test set
completedDatabase = input_data.Databases(databaseFileName=ALL_DATA_FILE_NAME,     targetLabel="MEDV", trainPercentage=0.70, valPercentage=0.20, testPercentage=0.10,
                  randomState=42, inputdataShuffle=True, batchDataShuffle=True)


# Parameters
learning_rate = 0.0001
training_epochs = 1000
batch_size = 5
display_step = 1

# Network Parameters
n_hidden_1 = 10 # 1st layer number of neurons
n_hidden_2 = 10 # 2nd layer number of neurons

n_input = 13 # number of features of my database
n_classes = 1 # one target value (float)

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


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

# Store layers weight &amp; bias
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.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# Initializing the variables
init = tf.initialize_all_variables()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    # Training cycle
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(completedDatabase.train.num_examples/batch_size)
        # Loop over all batches
        for i in range(total_batch):
            batch_x, batch_y = completedDatabase.train.next_batch(batch_size)
            # Run optimization op (backprop) and cost op (to get loss value)
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
                                                      y: batch_y})
            # Compute average loss
            avg_cost += c / total_batch
        # Display logs per epoch step
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch+1), "cost=", \
                "{:.9f}".format(avg_cost))
    print("Optimization Finished!")    
</code></pre>

<p>A couple of points. </p>

<p>Your model is quite shallow being only two layers.  Granted you'll need more data to train a larger model so I don't know how much data you have in the Boston data set.</p>

<p>What are your labels? That would better inform whether squared error is better for your model.</p>

<p>Also your learning rate is quite low.</p>

python machine-learning tensorflow multi-layer