tflearn_cnn_ex.py

import tflearn
from tflearn.layers.conv import conv_2d, maxpool_2d
from tflearn.layers.core import input_data ,dropout, fully_connected
from tflearn.layers.estimator import regression
import tflearn.datasets.mnist as mnist
X, Y, test_x, test_y = mnist.load_data(one_hot=True)
X = X.reshape([-1, 28, 28, 1])
test_x = test_x.reshape([-1, 28, 28, 1])

convnet = input_data(shape=[None, 28, 28, 1], name='input')
convnet = conv_2d(convnet, 32, 2, activation='relu')

convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 64, 2, activation='relu')

convnet = max_pool_2d(convnet, 2)
convnet = fully_connected(convnet, 10, activation='relu')

convnet=regression(convnet, optimizer='adam', learning_rate=0.01, loss='categorical_crossentropy', name='targets')
model = tflearn.DNN(convnet)

model.fit({'input': X}, {'targets': Y}, n_epoch=10, validation_set=({'input': test_x}, {'targets': test_y}),
    snapshot_step=500, show_metric=True, run_id='mnist')

model.save('quickest.model')
model.load('quickest.model')
model.predict(data)