TFLearn MNIST

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前提条件：已安装TensorFlow 1.0或以上版本

安装TFLearn

activate tensorflow (本人是conda环境)

pip install git+https://github.com/tflearn/tflearn.git

实现CNN

import tflearnfrom tflearn.layers.core import input_data, dropout, fully_connectedfrom tflearn.layers.conv import conv_2d, max_pool_2dfrom tflearn.layers.normalization import local_response_normalizationfrom tflearn.layers.estimator import regression# Data loading and preprocessingimport tflearn.datasets.mnist as mnistX, Y, testX, testY = mnist.load_data(one_hot=True)X = X.reshape([-1, 28, 28, 1])testX = testX.reshape([-1, 28, 28, 1])# Building convolutional networknetwork = input_data(shape=[None, 28, 28, 1], name='input')network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")network = max_pool_2d(network, 2)network = local_response_normalization(network)network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")network = max_pool_2d(network, 2)network = local_response_normalization(network)network = fully_connected(network, 128, activation='tanh')network = dropout(network, 0.8)network = fully_connected(network, 256, activation='tanh')network = dropout(network, 0.8)network = fully_connected(network, 10, activation='softmax')network = regression(network, optimizer='adam', learning_rate=0.01, loss='categorical_crossentropy', name='target')# Trainingmodel = tflearn.DNN(network, tensorboard_verbose=0)model.fit({'input': X}, {'target': Y}, n_epoch=20, validation_set=({'input': testX}, {'target': testY}), snapshot_step=100, show_metric=True, run_id='convnet_mnist')

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