运行代码：
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data # set random seed for comparing the two result calculations
tf.set_random_seed(1) # this is data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) # hyperparameters
lr = 0.001
training_iters = 100000
batch_size = 128 n_inputs = 28 # MNIST data input (img shape: 28*28)
n_steps = 28 # time steps
n_hidden_units = 128 # neurons in hidden layer
n_classes = 10 # MNIST classes (0-9 digits) # tf Graph input
x = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.float32, [None, n_classes]) # Define weights
weights = {
# (28, 128)
'in': tf.Variable(tf.random_normal([n_inputs, n_hidden_units])),
# (128, 10)
'out': tf.Variable(tf.random_normal([n_hidden_units, n_classes]))
}
biases = {
# (128, )
'in': tf.Variable(tf.constant(0.1, shape=[n_hidden_units, ])),
# (10, )
'out': tf.Variable(tf.constant(0.1, shape=[n_classes, ]))
} def RNN(X, weights, biases):
# hidden layer for input to cell # transpose the inputs shape from
# X ==> (128 batch * 28 steps, 28 inputs)
X = tf.reshape(X, [-1, n_inputs]) # into hidden
# X_in = (128 batch * 28 steps, 128 hidden)
X_in = tf.matmul(X, weights['in']) + biases['in']
# X_in ==> (128 batch, 28 steps, 128 hidden)
X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_units]) # cell
########################################## # basic LSTM Cell.
cell = tf.contrib.rnn.BasicLSTMCell(n_hidden_units)
# lstm cell is divided into two parts (c_state, h_state)
init_state = cell.zero_state(batch_size, dtype=tf.float32) outputs, final_state = tf.nn.dynamic_rnn(cell, X_in, initial_state=init_state, time_major=False) # unpack to list [(batch, outputs)..] * steps
outputs = tf.unstack(tf.transpose(outputs, [1,0,2]))
results = tf.matmul(outputs[-1], weights['out']) + biases['out'] # shape = (128, 10) return results pred = RNN(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
train_op = tf.train.AdamOptimizer(lr).minimize(cost) correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
step = 0
while step * batch_size < training_iters:
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
batch_xs = batch_xs.reshape([batch_size, n_steps, n_inputs])
sess.run([train_op], feed_dict={
x: batch_xs,
y: batch_ys,
})
if step % 20 == 0:
print(sess.run(accuracy, feed_dict={
x: batch_xs,
y: batch_ys,
}))
step += 1
运行结果：
