def get_mnist_data():
tf_data_sets = read_data_sets("../../../dat/mnist-tf", one_hot=False)
convert = lambda data_set: DataSet(
data_set.images.reshape((-1, 28, 28, 1)), data_set.labels)
return Data(train=convert(tf_data_sets.train),
validation=convert(tf_data_sets.validation),
test=convert(tf_data_sets.test))
@contact : [email protected] @desc : This tutorial will introduce how to combine TFLearn and Tensorflow, using TFLearn trainer with regular Tensorflow graph. """ from __future__ import print_function import tensorflow as tf import tflearn # -------------------------------------- # High-Level API: Using TFLearn wrappers # -------------------------------------- # Using MNIST Dataset import tflearn.datasets.mnist as mnist mnist_data = mnist.read_data_sets(one_hot=True) # User defined placeholders with tf.Graph().as_default(): # Placeholders for data and labels X = tf.placeholder(shape=(None, 784), dtype=tf.float32) Y = tf.placeholder(shape=(None, 10), dtype=tf.float32) net = tf.reshape(X, [-1, 28, 28, 1]) # Using TFLearn wrappers for network building net = tflearn.conv_2d(net, 32, 3, activation='relu') net = tflearn.max_pool_2d(net, 2) net = tflearn.local_response_normalization(net) net = tflearn.dropout(net, 0.8) net = tflearn.conv_2d(net, 64, 3, activation='relu')
#! /usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = "maxim"
import tensorflow as tf
tf.python.control_flow_ops = tf
import tflearn
import tflearn.data_utils as du
# Data loading and preprocessing
import tflearn.datasets.mnist as mnist
data = mnist.read_data_sets("/home/maxim/p/dat/mnist-tf", one_hot=True)
X, Y, valX, valY, testX, testY = data.train.images, data.train.labels, \
data.validation.images, data.validation.labels, \
data.test.images, data.test.labels
X = X.reshape([-1, 28, 28, 1])
valX = valX.reshape([-1, 28, 28, 1])
testX = testX.reshape([-1, 28, 28, 1])
X, mean = du.featurewise_zero_center(X)
valX = du.featurewise_zero_center(valX, mean)
testX = du.featurewise_zero_center(testX, mean)
# Building Residual Network
net = tflearn.input_data(shape=[None, 28, 28, 1])
net = tflearn.conv_2d(net, 64, 3, activation='relu', bias=False)
# Residual blocks
net = tflearn.residual_bottleneck(net, 3, 16, 64)
net = tflearn.residual_bottleneck(net, 1, 32, 128, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 32, 128)
"""
This tutorial will introduce how to combine TFLearn and Tensorflow, using
TFLearn trainer with regular Tensorflow graph.
"""
import tensorflow as tf
import tflearn
# --------------------------------------
# High-Level API: Using TFLearn wrappers
# --------------------------------------
# Using MNIST Dataset
import tflearn.datasets.mnist as mnist
mnist_data = mnist.read_data_sets(one_hot=True)
# User defined placeholders
with tf.Graph().as_default():
# Placeholders for data and labels
X = tf.placeholder(shape=(None, 784), dtype=tf.float32)
Y = tf.placeholder(shape=(None, 10), dtype=tf.float32)
net = tf.reshape(X, [-1, 28, 28, 1])
# Using TFLearn wrappers for network building
net = tflearn.conv_2d(net, 32, 3, activation='relu')
net = tflearn.max_pool_2d(net, 2)
net = tflearn.local_response_normalization(net)
net = tflearn.dropout(net, 0.8)
net = tflearn.conv_2d(net, 64, 3, activation='relu')
net = tflearn.max_pool_2d(net, 2)
"""
This tutorial will introduce how to combine TFLearn and Tensorflow, using
TFLearn trainer with regular Tensorflow graph.
"""
import tensorflow as tf
import tflearn
# --------------------------------------
# High-Level API: Using TFLearn wrappers
# --------------------------------------
import tflearn.datasets.mnist as mnist
mnist_data = mnist.read_data_sets('data/', one_hot=True)
# User define placeholders
with tf.Graph().as_default():
# placeholders for data and labels
X = tf.placeholder(shape=[None, 784], dtype=tf.float32)
Y = tf.placeholder(shape=[None, 10], dtype=tf.float32)
net = tf.reshape(X, [-1, 28, 28, 1])
# Using TFLearn wrappers for network building
net = tflearn.conv_2d(net, 32, 3, activation='relu')
net = tflearn.max_pool_2d(net, 2)
net = tflearn.local_response_normalization(net)
net = tflearn.dropout(net, 0.8)
net = tflearn.conv_2d(net, 64, 3, activation='relu')
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
mnist_data = mnist.read_data_sets(one_hot=True)
with tf.Graph().as_default():
# placeholders for data and labels
X = tf.placeholder(shape=(None, 784), dtype=tf.float32)
Y = tf.placeholder(shape=(None, 10), dtype=tf.float32)
net = tf.reshape(X, [-1, 28, 28, 1])
net = tflearn.conv_2d(net, 32, 3, activation='relu')
net = tflearn.max_pool_2d(net, 2)
net = tflearn.local_response_normalization(net)
net = tflearn.dropout(net, 0.8)
net = tflearn.conv_2d(net, 64, 3, activation='relu')
net = tflearn.max_pool_2d(net, 2)
net = tflearn.local_response_normalization(net)
net = tflearn.dropout(net, 0.8)
net = tflearn.fully_connected(net, 128, activation='tanh')
net = tflearn.dropout(net, 0.8)
net = tflearn.fully_connected(net, 256, activation='tanh')
net = tflearn.dropout(net, 0.8)
net = tflearn.fully_connected(net, 10, activation='linear')
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(net, Y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01).minimize(loss)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
batch_size = 128
for epoch in range(2):
avg_cost = 0
total_batch = int(mnist_data.train.num_examples / batch_size)
def load_data(one_hot=False):
mnist_data = mnist.read_data_sets(SOURCE + 'mnist/data/', one_hot=one_hot)
return mnist_data.train.images, mnist_data.train.labels, mnist_data.test.images, mnist_data.test.labels
