TensorX

A minimalist utility library to build neural network models in TensorFlow with minimum verbose (and without unnecessary levels of abstraction). TensorX provides a minimum set of utility parts to build computation graphs.

Pip installation

sudo pip3 install --upgrade git+https://github.com/davidelnunes/tensorx.git

Getting Started: MNIST example with TensorX

import tensorflow as tf
from tensorx.parts.core import NeuralNetwork,Layer
from tensorflow.examples.tutorials.mnist import input_data

# load MNIST data
mnist = input_data.read_data_sets('MNIST_data', one_hot = True)

ss = tf.InteractiveSession()

n_inputs = 784
x = tf.placeholder(tf.float32, [None, n_inputs], name = "x")

# create neural network with tensorx
input_layer = Layer(n_units = n_inputs, activation = x)
network = NeuralNetwork(input_layer)
output_layer = Layer(10, tf.nn.softmax)
network.add_layer(output_layer, biased=True)

# train
target_output = tf.placeholder("float", shape=[None, 10])
# loss function
network_output = network.output()
cross_entropy = -tf.reduce_sum(target_output*tf.log(tf.clip_by_value(network_output,1e-50,1.0)))
train_step_rate = 0.003 # default: 0.003 -> accuracy ~ 0.9162 (step 999)
train_step = tf.train.GradientDescentOptimizer(train_step_rate).minimize(cross_entropy)

# test
correct_prediction = tf.equal(tf.argmax(network_output,1), tf.argmax(target_output,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

# run train and test
tf.global_variables_initializer().run()

n_steps = 1000
for i in range(n_steps):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    feed = {x: batch_xs, target_output: batch_ys}
    ss.run(train_step, feed_dict=feed)
    if i % 10 == 0:  # Record summary data, and the accuracy
        feed = {x: mnist.test.images, target_output: mnist.test.labels}
        acc = ss.run(accuracy, feed_dict=feed)
        if i == 0: # first step
            print("Accuracy at step {0:4d}: {1:.04f} | Error: {2:.2f} %".format(i, acc, (1 - acc) * 100))
        else:
            print("Accuracy at step {0:4d}: {1:.04f}".format(i, acc))

# last step
feed = {x: mnist.test.images, target_output: mnist.test.labels}
acc = ss.run(accuracy, feed_dict=feed)
print("Accuracy at step {0:4d}: {1:.04f} | Error: {2:.2f} %".format(i, acc, (1 - acc) * 100))

ss.close()

Licence

Copyright 2017 Davide Nunes

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.