用RNN做MNIST分类
1.前言
RNN常用作NLP中,像图片生成文字、自动生成古诗词等。这篇文章用RNN做MNIST手写数字识别,分类效果虽然没有CNN效果好,但准确率也能够达到96%。
2.环境
Mac os系统,python:3.5,Keras
3.代码实现
import numpy as np
np.random.seed(1337)
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import SimpleRNN, Activation, Dense
from keras.optimizers import Adam
TIME_STEPS = 28
INPUT_SIZE = 28
BATCH_SIZE = 50
BATCH_INDEX = 0
OUTPUT_SIZE = 10
CELL_SIZE = 50
LR = 0.001
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# data pre-processing
X_train = X_train.reshape(-1, 28, 28) / 255. # normalize
X_test = X_test.reshape(-1, 28, 28) / 255. # normalize
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)
# build RNN model
model = Sequential()
# RNN cell
model.add(SimpleRNN(
batch_input_shape=(None, TIME_STEPS, INPUT_SIZE),
output_dim=CELL_SIZE,
unroll=True,
))
# output layer
model.add(Dense(OUTPUT_SIZE))
model.add(Activation('softmax'))
# optimizer
adam = Adam(LR)
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
# training
for step in range(40001):
X_batch = X_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :, :]
Y_batch = y_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :]
cost = model.train_on_batch(X_batch, Y_batch)
BATCH_INDEX += BATCH_SIZE
BATCH_INDEX = 0 if BATCH_INDEX >= X_train.shape[0] else BATCH_INDEX
if step % 500 == 0:
cost, accuracy = model.evaluate(X_test, y_test, batch_size=y_test.shape[0], verbose=False)
print('test cost: ', cost, 'test accuracy: ', accuracy)