# -*- coding: utf-8 -*- """ Deep Neural Network for MNIST dataset classification task. References: Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998. Links: [MNIST Dataset] http://yann.lecun.com/exdb/mnist/ """ from __future__ import division, print_function, absolute_import import tflearn # Data loading and preprocessing import tflearn.datasets.mnist as mnist X, Y, testX, testY = mnist.load_data(one_hot=True) # Building deep neural network input_layer = tflearn.input_data(shape=[None, 784]) dense1 = tflearn.fully_connected(input_layer, 64, activation='tanh', regularizer='L2', weight_decay=0.001) dropout1 = tflearn.dropout(dense1, 0.8) dense2 = tflearn.fully_connected(dropout1, 64, activation='tanh', regularizer='L2', weight_decay=0.001) dropout2 = tflearn.dropout(dense2, 0.8) softmax = tflearn.fully_connected(dropout2, 10, activation='softmax') # Regression using SGD with learning rate decay and Top-3 accuracy sgd = tflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=1000) top_k = tflearn.metrics.Top_k(3) net = tflearn.regression(softmax, optimizer=sgd, metric=top_k, loss='categorical_crossentropy') # Training model = tflearn.DNN(net, tensorboard_verbose=0) model.fit(X, Y, n_epoch=20, validation_set=(testX, testY), show_metric=True, run_id="dense_model")
from：https://github.com/tflearn/tflearn/blob/master/examples/images/dnn.py