本文整理汇总了Python中dataset.Dataset.next_batch方法的典型用法代码示例。如果您正苦于以下问题：Python Dataset.next_batch方法的具体用法？Python Dataset.next_batch怎么用？Python Dataset.next_batch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.next_batch方法的2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: main
# 需要导入模块: from dataset import Dataset [as 别名]
# 或者: from dataset.Dataset import next_batch [as 别名]
def main():
# Dataset path
train_list = "/path/to/data/flickr_style/train.txt"
test_list = "/path/to/data/flickr_style/test.txt"
# Learning params
learning_rate = 0.001
training_iters = 12800 # 10 epochs
batch_size = 50
display_step = 20
test_step = 640 # 0.5 epoch
# Network params
n_classes = 20
keep_rate = 0.5
# Graph input
x = tf.placeholder(tf.float32, [batch_size, 227, 227, 3])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_var = tf.placeholder(tf.float32)
# Model
pred = Model.alexnet(x, keep_var)
# Loss and optimizer
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
# Evaluation
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Init
init = tf.initialize_all_variables()
# Load dataset
dataset = Dataset(train_list, test_list)
# Launch the graph
with tf.Session() as sess:
print "Init variable"
sess.run(init)
# Load pretrained model
load_with_skip("caffenet.npy", sess, ["fc8"]) # Skip weights from fc8
print "Start training"
step = 1
while step < training_iters:
batch_xs, batch_ys = dataset.next_batch(batch_size, "train")
sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_var: keep_rate})
# Display testing status
if step % test_step == 0:
test_acc = 0.0
test_count = 0
for _ in range(int(dataset.test_size / batch_size)):
batch_tx, batch_ty = dataset.next_batch(batch_size, "test")
acc = sess.run(accuracy, feed_dict={x: batch_tx, y: batch_ty, keep_var: 1.0})
test_acc += acc
test_count += 1
test_acc /= test_count
print >> sys.stderr, "{} Iter {}: Testing Accuracy = {:.4f}".format(datetime.now(), step, test_acc)
# Display training status
if step % display_step == 0:
acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_var: 1.0})
batch_loss = sess.run(loss, feed_dict={x: batch_xs, y: batch_ys, keep_var: 1.0})
print >> sys.stderr, "{} Iter {}: Training Loss = {:.4f}, Accuracy = {:.4f}".format(
datetime.now(), step, batch_loss, acc
)
step += 1
print "Finish!"示例2: range
# 需要导入模块: from dataset import Dataset [as 别名]
# 或者: from dataset.Dataset import next_batch [as 别名]
ce_summ = tf.scalar_summary('cross entropy', cross_entropy)
train_step = tf.train.AdamOptimizer(LEARNING_RATE).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
acc_summ = tf.scalar_summary('accuracy', accuracy)
merged = tf.merge_all_summaries()
writer = tf.train.SummaryWriter('/tmp/vitruvian_logs', sess.graph_def)
saver = tf.train.Saver()
if TRAIN:
sess.run(tf.initialize_all_variables())
for i in range(ITERATIONS):
batch_x, batch_y = train_dataset.next_batch(BATCH_SIZE)
feed = {x: batch_x, y_: batch_y, keep_prob: 1.0}
train_accuracy = accuracy.eval(feed_dict={
x: batch_x, y_: batch_y, keep_prob: 1.0})
loss_val = cross_entropy.eval(feed_dict={
x: batch_x, y_: batch_y, keep_prob: 1.0})
print("step %d, training accuracy %g, loss %g"%(i, train_accuracy, loss_val))
summary = merged.eval(feed_dict={
x: batch_x, y_: batch_y, keep_prob: 1.0})
writer.add_summary(summary, i)
if i%1000 == 0:
save_path = saver.save(sess, 'model_%d.ckpt' % i)
print("model saved in file: %s" % save_path)