from __future__ import division, print_function, absolute_import
"""
TFLearn built-in ops make Tensorflow graphs writing faster and more readable. So, similar to layers, built-in ops are fully compatible with any TensorFlow expression. The following code example shows how to use them along with pure Tensorflow API.
This tutorial will introduce how to combine TFLearn built-in ops with any
Tensorflow graph.
"""
import tensorflow as tf
import tflearn
# ----------------------------------
# Using TFLearn built-in ops example
# ----------------------------------
# Using MNIST Dataset
import tflearn.datasets.mnist as mnist
trainX, trainY, testX, testY = mnist.load_data(one_hot=True)
# User defined placeholders
with tf.Graph().as_default():
# Model variables
X = tf.placeholder("float", [None, 784])
Y = tf.placeholder("float", [None, 10])
W1 = tf.Variable(tf.random_normal([784, 256]))
W2 = tf.Variable(tf.random_normal([256, 256]))
W3 = tf.Variable(tf.random_normal([256, 10]))
b1 = tf.Variable(tf.random_normal([256]))
b2 = tf.Variable(tf.random_normal([256]))
b3 = tf.Variable(tf.random_normal([10]))
# Multilayer perceptron
def dnn(x):
# Using TFLearn PReLU activations ops
x = tflearn.prelu(tf.add(tf.matmul(x, W1), b1))
tflearn.summaries.monitor_activation(x) # Monitor activation
x = tflearn.prelu(tf.add(tf.matmul(x, W2), b2))
tflearn.summaries.monitor_activation(x) # Monitor activation
x = tf.nn.softmax(tf.add(tf.matmul(x, W3), b3))
return x
net = dnn(X)
# Using objective ops from TFLearn to compute crossentropy
loss = tflearn.categorical_crossentropy(net, Y)
# Using metric ops from TFLearn to compute accuracy
acc = tflearn.metrics.accuracy_op(net, Y)
# Using TFLearn SGD Optimizer class
optimizer = tflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=200)
# Because of lr decay, it is required to first build the Optimizer with
# the step tensor that will monitor training step.
# (Note: When using TFLearn estimators wrapper, build is self managed,
# so only using above `Optimizer` class as `DNN` optimizer arg is enough).
step = tflearn.variable("step", initializer='zeros', shape=[])
optimizer.build(step_tensor=step)
optim_tensor = optimizer.get_tensor()
# Using TFLearn Trainer
# Define a training op (op for backprop, only need 1 in this model)
trainop = tflearn.TrainOp(loss=loss, optimizer=optim_tensor,
metric=acc, batch_size=128,
step_tensor=step)
# Create Trainer, providing all training ops. Tensorboard logs stored
# in /tmp/tflearn_logs/. It is possible to change verbose level for more
# details logs about gradients, variables etc...
trainer = tflearn.Trainer(train_ops=trainop, tensorboard_verbose=0)
# Training for 10 epochs.
trainer.fit({X: trainX, Y: trainY}, val_feed_dicts={X: testX, Y: testY},
n_epoch=10, show_metric=True)
