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- import tensorflow as tf
-
- # hyperparameters
- n_neurons = 128
- learning_rate = 0.001
- batch_size = 128
- n_epochs = 10
- # parameters
- n_steps = 28 # 28 rows
- n_inputs = 28 # 28 cols
- n_outputs = 10 # 10 classes
- # build a rnn model
- X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
- y = tf.placeholder(tf.int32, [None])
- cell = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)
- output, state = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
- logits = tf.layers.dense(state, n_outputs)
- cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
- loss = tf.reduce_mean(cross_entropy)
- optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
- prediction = tf.nn.in_top_k(logits, y, 1)
- accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))
-
- # input data
- from tensorflow.examples.tutorials.mnist import input_data
- mnist = input_data.read_data_sets("MNIST_data/")
- X_test = mnist.test.images # X_test shape: [num_test, 28*28]
- X_test = X_test.reshape([-1, n_steps, n_inputs])
- y_test = mnist.test.labels
-
- # initialize the variables
- init = tf.global_variables_initializer()
- # train the model
- with tf.Session() as sess:
- sess.run(init)
- n_batches = mnist.train.num_examples // batch_size
- for epoch in range(n_epochs):
- for batch in range(n_batches):
- X_train, y_train = mnist.train.next_batch(batch_size)
- X_train = X_train.reshape([-1, n_steps, n_inputs])
- sess.run(optimizer, feed_dict={X: X_train, y: y_train})
- loss_train, acc_train = sess.run(
- [loss, accuracy], feed_dict={X: X_train, y: y_train})
- print('Epoch: {}, Train Loss: {:.3f}, Train Acc: {:.3f}'.format(
- epoch + 1, loss_train, acc_train))
- loss_test, acc_test = sess.run(
- [loss, accuracy], feed_dict={X: X_test, y: y_test})
- print('Test Loss: {:.3f}, Test Acc: {:.3f}'.format(loss_test, acc_test))
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