Python DataSet.get_test_set_size方法代码示例

# 需要导入模块: from dataset import DataSet [as 别名]
# 或者: from dataset.DataSet import get_test_set_size [as 别名]

#.........这里部分代码省略.........
        trainable=True, name='biases')
      pool2_flat = tf.reshape(self.pool2, [-1, shape])
      fc1l = tf.nn.bias_add(tf.matmul(pool2_flat, fc1w), fc1b)
      self.fc1 = tf.nn.relu(fc1l)
      self.dropout1 = tf.nn.dropout(self.fc1, keep_prob=self.keep_prob, name='dropout1')
      self.parameters += [fc1w, fc1b]

    # fc2
    with tf.name_scope('fc2') as scope:
      fc2w = tf.Variable(tf.random_normal([128, 10],
        dtype=tf.float32, stddev=1e-1), name='weights')
      fc2b = tf.Variable(tf.constant(1.0, shape=[10], dtype=tf.float32),
        trainable=True, name='biases')
      fc2l = tf.nn.bias_add(tf.matmul(self.dropout1, fc2w), fc2b)
      self.logits = tf.nn.relu(fc2l)
      self.parameters += [fc2w, fc2b]

  def load_weights(weights, sess):
    None

  def train(self, learning_rate, training_epochs, batch_size, keep_prob):
    # Load dataset for training and testing
    self.dataset = DataSet()

    # Define size of output
    self.Y = tf.placeholder(tf.float32, [None, 10], name='Y')
    # Define cost function
    self.cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.logits, labels=self.Y))
    # Define optimization method
    self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(self.cost)

    # Start logger
    if self.log:
        tf.summary.scalar('cost', self.cost)
        self.merged = tf.summary.merge_all()
        self.train_writer = tf.summary.FileWriter('./log_train', self.sess.graph)

    self.sess.run(tf.global_variables_initializer())
    self.sess.run(tf.local_variables_initializer())

    print('Training...')
    weights = []
    # For each epoch, feed training data and perform updating parameters
    for epoch in range(training_epochs):
        avg_cost = 0
        # Number of batches = size of training set / batch_size
        total_batch = int(self.dataset.get_train_set_size() / batch_size)

        # For each batch 
        for i in range(total_batch + 1):
            # Get next batch to feed to the network
            batch_xs, batch_ys = self.dataset.next_batch(batch_size)
            feed_dict = {
                self.X: batch_xs.reshape([batch_xs.shape[0], 28, 28, 1]),
                self.Y: batch_ys,
                self.keep_prob: keep_prob
            }

            weights, summary, c, _ = self.sess.run([self.parameters, self.merged, self.cost, self.optimizer],
                                                   feed_dict=feed_dict)
            avg_cost += c / total_batch

        if self.log:
            self.train_writer.add_summary(summary, epoch + 1)

        print('Epoch:', '%02d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))

    print('Training finished!')

    saver = tf.train.Saver()
    save_path = saver.save(self.sess, model_dir + "/mnist_lenet.ckpt")
    print("Trainned model is saved in file: %s" % save_path)

  def evaluate(self, batch_size, keep_prob):

    self.correct_prediction = tf.equal(tf.argmax(self.logits, 1), tf.argmax(self.Y, 1))
    self.accuracy = tf.reduce_mean(tf.cast(self.correct_prediction, tf.float32))

    N = self.dataset.get_test_set_size()
    print('test.size', N);
    correct_sample = 0
    for i in range(0, N, batch_size):
        batch_xs, batch_ys = self.dataset.next_batch_test(batch_size)

        N_batch = batch_xs.shape[0]

        feed_dict = {
            self.X: batch_xs.reshape([N_batch, 28, 28, 1]),
            self.Y: batch_ys,
            self.keep_prob: keep_prob
        }

        correct = self.sess.run(self.accuracy, feed_dict=feed_dict)
        correct_sample += correct * N_batch

    test_accuracy = correct_sample / N

    print("\nAccuracy Evaluates")
    print("-" * 30)
    print('Test Accuracy:', test_accuracy)