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# This script is used to train the model. It repeats indefinitely and saves the
# model every so often to a checkpoint.
# Press Ctrl+C when you feel that training has gone on long enough (since this is
# only a simple model it takes less than a minute to train, but a training a deep l
# earning model could take days).
import os
import numpy as np
import tensorflow as tf
checkpoint_dir = "/tmp/voice/"
print_every = 1000
save_every = 10000
num_inputs = 20
num_classes = 1
# Load the training data.
X_train = np.load("X_train.npy")
y_train = np.load("y_train.npy")
print("Training set size:", X_train.shape)
# Below we'll define the computational graph using TensorFlow. The different parts
# of the model are grouped into different "scopes", making it easier to understand
# what each part is doing.
# Hyperparameters let you configure the model and how it is trained. They're
# called "hyper" parameters because unlike the regular parameters they are not
# learned by the model -- you have to set them to appropriate values yourself.
# The learning_rate tells the optimizer how big of a steps it should take.
# Regularization is used to prevent overfitting on the training set.
with tf.name_scope("hyperparameters"):
regularization = tf.placeholder(tf.float32, name="regularization")
learning_rate = tf.placeholder(tf.float32, name="learning-rate")
# This is where we feed the training data (and later the test data) into the model.
# In this dataset there are 20 features, so x is a matrix with 20 columns. Its number
# of rows is None because it depends on how many examples at a time we put into this
# matrix. This is a binary classifier so for every training example, y gives a single
# output: 1 = male, 0 = female.
with tf.name_scope("inputs"):
x = tf.placeholder(tf.float32, [None, num_inputs], name="x-input")
y = tf.placeholder(tf.float32, [None, num_classes], name="y-input")
# The parameters that we'll learn consist of W, a weight matrix, and b, a vector
# of bias values. (Actually, b is just a single value since the classifier has only
# one output. For a classifier that can recognize multiple classes, b would have as
# many elements as there are classes.)
with tf.name_scope("model"):
W = tf.Variable(tf.zeros([num_inputs, num_classes]), name="W")
b = tf.Variable(tf.zeros([num_classes]), name="b")
# The output is the probability the speaker is male. If this is greater than
# 0.5, we consider the speaker to be male, otherwise female.
y_pred = tf.sigmoid(tf.matmul(x, W) + b, name="y_pred")
# This is a logistic classifier, so the loss function is the logistic loss.
with tf.name_scope("loss-function"):
loss = tf.losses.log_loss(labels=y, predictions=y_pred)
# Add L2 regularization to the loss.
loss += regularization * tf.nn.l2_loss(W)
# Use the ADAM optimizer to minimize the loss.
with tf.name_scope("train"):
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss)
# For doing inference on new data for which we don't have labels.
with tf.name_scope("inference"):
inference = tf.to_float(y_pred > 0.5, name="inference")
# The accuracy operation computes the % correct on a dataset with known labels.
with tf.name_scope("score"):
correct_prediction = tf.equal(inference, y)
accuracy = tf.reduce_mean(tf.to_float(correct_prediction), name="accuracy")
init = tf.global_variables_initializer()
# For writing training checkpoints and reading them back in.
saver = tf.train.Saver()
with tf.Session() as sess:
# Write the graph definition to a file. We'll load this in the script.
tf.train.write_graph(sess.graph_def, checkpoint_dir, "graph.pb", False)
# Reset W and b to zero.
# Sanity check: the initial loss should be 0.693146, which is -ln(0.5).
loss_value =, feed_dict={x: X_train, y: y_train, regularization: 0})
print("Initial loss:", loss_value)
# Loop forever:
step = 0
while True:
# We randomly shuffle the examples every time we train.
perm = np.arange(len(X_train))
X_train = X_train[perm]
y_train = y_train[perm]
# Run the optimizer over the entire training set at once. For larger datasets
# you would train in batches of 100-1000 examples instead of the entire thing.
feed = {x: X_train, y: y_train, learning_rate: 1e-2, regularization: 1e-5}, feed_dict=feed)
# Print the loss once every so many steps. Because of the regularization,
# at some point the loss won't become smaller anymore. At that point, it's
# safe to press Ctrl+C to stop the training.
if step % print_every == 0:
train_accuracy, loss_value =[accuracy, loss], feed_dict=feed)
print("step: %4d, loss: %.4f, training accuracy: %.4f" % \
(step, loss_value, train_accuracy))
step += 1
# Save the model. You should only press Ctrl+C after you see this message.
if step % save_every == 0:
checkpoint_file = os.path.join(checkpoint_dir, "model"), checkpoint_file)
print("*** SAVED MODEL ***")