本文整理汇总了Python中dataset.DataSet.get_train_set_size方法的典型用法代码示例。如果您正苦于以下问题：Python DataSet.get_train_set_size方法的具体用法？Python DataSet.get_train_set_size怎么用？Python DataSet.get_train_set_size使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类dataset.DataSet的用法示例。
在下文中一共展示了DataSet.get_train_set_size方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: LeNet
# 需要导入模块: from dataset import DataSet [as 别名]
# 或者: from dataset.DataSet import get_train_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)