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tflearn/examples/basics/use_hdf5.py
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# -*- coding: utf-8 -*- | |
""" | |
Example on how to use HDF5 dataset with TFLearn. HDF5 is a data model, | |
library, and file format for storing and managing data. It can handle large | |
dataset that could not fit totally in ram memory. Note that this example | |
just give a quick compatibility demonstration. In practice, there is no so | |
real need to use HDF5 for small dataset such as CIFAR-10. | |
""" | |
from __future__ import division, print_function, absolute_import | |
import tflearn | |
from tflearn.layers.core import * | |
from tflearn.layers.conv import * | |
from tflearn.data_utils import * | |
from tflearn.layers.normalization import * | |
from tflearn.layers.estimator import regression | |
# CIFAR-10 Dataset | |
from tflearn.datasets import cifar10 | |
(X, Y), (X_test, Y_test) = cifar10.load_data() | |
Y = to_categorical(Y) | |
Y_test = to_categorical(Y_test) | |
# Create a hdf5 dataset from CIFAR-10 numpy array | |
import h5py | |
h5f = h5py.File('data.h5', 'w') | |
h5f.create_dataset('cifar10_X', data=X) | |
h5f.create_dataset('cifar10_Y', data=Y) | |
h5f.create_dataset('cifar10_X_test', data=X_test) | |
h5f.create_dataset('cifar10_Y_test', data=Y_test) | |
h5f.close() | |
# Load hdf5 dataset | |
h5f = h5py.File('data.h5', 'r') | |
X = h5f['cifar10_X'] | |
Y = h5f['cifar10_Y'] | |
X_test = h5f['cifar10_X_test'] | |
Y_test = h5f['cifar10_Y_test'] | |
# Build network | |
network = input_data(shape=[None, 32, 32, 3], dtype=tf.float32) | |
network = conv_2d(network, 32, 3, activation='relu') | |
network = max_pool_2d(network, 2) | |
network = conv_2d(network, 64, 3, activation='relu') | |
network = conv_2d(network, 64, 3, activation='relu') | |
network = max_pool_2d(network, 2) | |
network = fully_connected(network, 512, activation='relu') | |
network = dropout(network, 0.5) | |
network = fully_connected(network, 10, activation='softmax') | |
network = regression(network, optimizer='adam', | |
loss='categorical_crossentropy', | |
learning_rate=0.001) | |
# Training | |
model = tflearn.DNN(network, tensorboard_verbose=0) | |
model.fit(X, Y, n_epoch=50, shuffle=True, validation_set=(X_test, Y_test), | |
show_metric=True, batch_size=96, run_id='cifar10_cnn') | |
h5f.close() |