tensorflow_example

Complexity

Total Complexity

3

Size/Duplication

Total Lines	89
Duplicated Lines	0 %

Importance

Changes

0

1 Function

Rating	Name	Duplication	Size	Complexity
B	cnn()	0	59	3

from __future__ import division, print_function, absolute_import
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

from hyperactive import Hyperactive

mnist = input_data.read_data_sets("/tmp/data/", one_hot=False)

learning_rate = 0.001
num_steps = 500
batch_size = 128

num_input = 784
num_classes = 10
dropout = 0.25

X_train = mnist.train.images
y_train = mnist.train.labels

X_test = mnist.test.images
y_test = mnist.test.labels


def cnn(para, X_train, y_train):

  def conv_net(x_dict, n_classes, dropout, reuse, is_training):
      with tf.variable_scope('ConvNet', reuse=reuse):
          x = x_dict['images']
          x = tf.reshape(x, shape=[-1, 28, 28, 1])
          conv1 = tf.layers.conv2d(x, para["filters_0"], 5, activation=tf.nn.relu)
          conv1 = tf.layers.max_pooling2d(conv1, 2, 2)
          conv2 = tf.layers.conv2d(conv1, para["filters_1"], 3, activation=tf.nn.relu)
          conv2 = tf.layers.max_pooling2d(conv2, 2, 2)
          fc1 = tf.contrib.layers.flatten(conv2)
          fc1 = tf.layers.dense(fc1, para["dense_0"])
          fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)
          out = tf.layers.dense(fc1, n_classes)

      return out

  def model_fn(features, labels, mode):
      logits_train = conv_net(features, num_classes, dropout, reuse=False,
                              is_training=True)
      logits_test = conv_net(features, num_classes, dropout, reuse=True,
                            is_training=False)

      pred_classes = tf.argmax(logits_test, axis=1)
      pred_probas = tf.nn.softmax(logits_test)

      if mode == tf.estimator.ModeKeys.PREDICT:
          return tf.estimator.EstimatorSpec(mode, predictions=pred_classes)

      loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
          logits=logits_train, labels=tf.cast(labels, dtype=tf.int32)))
      optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
      train_op = optimizer.minimize(loss_op,
                                    global_step=tf.train.get_global_step())

      acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)

      estim_specs = tf.estimator.EstimatorSpec(
          mode=mode,
          predictions=pred_classes,
          loss=loss_op,
          train_op=train_op,
          eval_metric_ops={'accuracy': acc_op})

      return estim_specs

  model = tf.estimator.Estimator(model_fn)

  input_fn = tf.estimator.inputs.numpy_input_fn(
      x={'images': X_train}, y=y_train,
      batch_size=batch_size, num_epochs=None, shuffle=True)
  model.train(input_fn, steps=num_steps)

  input_fn = tf.estimator.inputs.numpy_input_fn(
      x={'images': X_test}, y=y_test,
      batch_size=batch_size, shuffle=False)
  e = model.evaluate(input_fn)

  return float(e['accuracy'])


search_config = {cnn: {"filters_0": [16, 32, 64], "filters_1" : [16, 32, 64], "dense_0": range(100, 2000, 100)}}

opt = Hyperactive(search_config, n_iter=20)
opt.search(X_train, y_train)