Load data on tensorflow

data_loaded_tensorflow.py

"""
From Jeremie :)
"""
from __future__ import print_function
 
import numpy
import tensorflow as tf
 
rng = numpy.random
 
# Parameters
learning_rate = 0.01
training_epochs = 100
display_step = 5
batch_size = 2
 
data_a = numpy.asarray([
    [0, 0, 0],
    [1, 1, 1],
    [2, 2, 2],
    [3, 3, 3],
    [4, 4, 4],
    [5, 5, 5],
    [6, 6, 6],
    [7, 7, 7],
    [8, 8, 8],
    [9, 9, 9],
])
 
data_y = numpy.asarray([
    [0, 1],
    [0, 1],
    [0, 1],
    [0, 1],
    [0, 1],
    [1, 0],
    [1, 0],
    [1, 0],
    [1, 0],
    [1, 0]
])
 
n_hidden_1 = 10  # 1st layer number of features
n_hidden_2 = 10  # 2nd layer number of features
n_input = 3  # MNIST data input (img shape: 28*28)
n_classes = 2  # MNIST total classes (0-9 digits)
 
X = tf.placeholder(tf.float32, [None, n_input])  # 3 features
Y = tf.placeholder(tf.float32, [None, n_classes])  # binary classification
 
W = tf.Variable(tf.random_normal(shape=(3, 2), mean=1, stddev=0.1), dtype=tf.float32)
b = tf.Variable(tf.zeros([2]))
 
 
def mlp(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
 
 
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]))
}
 
prediction = mlp(X, weights, biases)
 
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
 
init = tf.global_variables_initializer()
 
# Launch the graph
with tf.Session() as sess:
    sess.run(init)
    # Training cycle
 
    for step in range(training_epochs):
        offset = (step * batch_size) % (data_y.shape[0] - batch_size)
        batch_data = data_a[offset:(offset + batch_size), :]
        batch_labels = data_y[offset:(offset + batch_size)]
        feed_dict = {X: batch_data, Y: batch_labels}
        _, c = sess.run([optimizer, cost], feed_dict=feed_dict)
 
    print("Optimization Finished!")
 
    # Test model
    correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
    # Calculate accuracy
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print("Accuracy:", accuracy.eval({X: data_a, Y: data_y}))