tfl_cnn.py

import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
import tflearn.datasets.mnist as mnist
import numpy as np

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])

# Building convolutional convnet
convnet = input_data(shape=[None, 28, 28, 1], name='input')
# http://tflearn.org/layers/conv/
# http://tflearn.org/activations/
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, 1024, activation='relu')
convnet = dropout(convnet, 0.8)

convnet = fully_connected(convnet, 10, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=0.01, loss='categorical_crossentropy', name='targets')

model = tflearn.DNN(convnet)
model.load('quicktest.model')

print( np.round(model.predict([test_x[1]])[0]) )
print(test_y[1])