本文整理汇总了Python中reader.Reader.read_training_data方法的典型用法代码示例。如果您正苦于以下问题：Python Reader.read_training_data方法的具体用法？Python Reader.read_training_data怎么用？Python Reader.read_training_data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reader.Reader的用法示例。
在下文中一共展示了Reader.read_training_data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: __init__
# 需要导入模块: from reader import Reader [as 别名]
# 或者: from reader.Reader import read_training_data [as 别名]
def __init__(self, train):
dataset_number = env.DATASET_NUMBER
num_dataset_to_use = env.NUM_DATASET_TO_USE
chords_in_vector = env.CHORDS_IN_BAR * dataset_number
chord_length = env.CHORD_LENGTH
num_layers = env.NUM_LAYERS
num_hidden = env.NUM_HIDDEN
batch_size = env.BATCH_SIZE
epoch = env.EPOCH
dropout_pb = env.DROPOUT_PB
with tf.variable_scope(str(dataset_number) + str(num_dataset_to_use) + str(chords_in_vector) + str(chord_length)):
cell_type = tf.nn.rnn_cell.GRUCell
# Number of examples, number of input, dimension of each input
data = tf.placeholder(tf.float64, [None, chords_in_vector, chord_length])
target = tf.placeholder(tf.float64, [None, 2])
cell = cell_type(num_hidden)
reader = Reader()
if train:
reader.read_training_data(dataset_number, num_dataset_to_use)
reader.read_testing_data(dataset_number, num_dataset_to_use)
# make sure it has the correct format for the RNN
reader.convert_to_rnn_format(chords_in_vector, chord_length)
self.model = model = MultiRNNModel(cell, data, target, train, batch_size, epoch, dropout_pb, num_hidden, num_layers, reader.training_attributes, reader.training_labels, reader.testing_attributes, reader.testing_labels)
else:
#reader.read_training_data(dataset_number, 0)
#reader.read_testing_data(dataset_number, num_dataset_to_use)
# make sure it has the correct format for the RNN
#reader.convert_to_rnn_format(chords_in_vector, chord_length)
self.model = model = MultiRNNModel(cell, data, target, train, batch_size, epoch, dropout_pb, num_hidden, num_layers)示例2: Reader
# 需要导入模块: from reader import Reader [as 别名]
# 或者: from reader.Reader import read_training_data [as 别名]
from chord_parser import *
from reader import Reader
from random import randint
from pprint import pprint
np.set_printoptions(precision=6, suppress=True)
# GRU
dataset_number = 8
num_dataset_to_use = 10000
chords_in_vector = 4 * dataset_number
chord_length = 33
reader = Reader()
reader.read_training_data(dataset_number, num_dataset_to_use)
reader.read_testing_data(dataset_number, num_dataset_to_use)
C = 1.0
gamma = 0.1
model = SVMModel(C, gamma)
model.train(reader.training_attributes, reader.training_labels)
train_error = model.test(reader.training_attributes, reader.training_labels)
test_error = model.test(reader.testing_attributes, reader.testing_labels)
print("Train Accuracy {:2.2f}%".format(train_error * 100))
print("Test Accuracy {:2.2f}%".format(test_error * 100))