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This tutorial will introduce how to combine TFLearn and Tensorflow, using
TFLearn wrappers regular Tensorflow expressions.
import tensorflow.compat.v1 as tf
import tflearn
# ----------------------------
# Utils: Using TFLearn Trainer
# ----------------------------
# Loading MNIST complete dataset
import tflearn.datasets.mnist as mnist
trainX, trainY, testX, testY = mnist.load_data(one_hot=True)
# Define a dnn using Tensorflow
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):
x = tf.nn.tanh(tf.add(tf.matmul(x, W1), b1))
x = tf.nn.tanh(tf.add(tf.matmul(x, W2), b2))
x = tf.add(tf.matmul(x, W3), b3)
return x
net = dnn(X)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=net, labels=Y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
accuracy = tf.reduce_mean(
tf.cast(tf.equal(tf.argmax(net, 1), tf.argmax(Y, 1)), tf.float32),
# Using TFLearn Trainer
# Define a training op (op for backprop, only need 1 in this model)
trainop = tflearn.TrainOp(loss=loss, optimizer=optimizer,
metric=accuracy, batch_size=128)
# 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.{X: trainX, Y: trainY}, val_feed_dicts={X: testX, Y: testY},
n_epoch=10, show_metric=True)