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)
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))
#####################################################################
import csv
import cv2
import numpy as np
import time
import smtplib
from email.mime.text import MIMEText
from reader import Reader
SENDING_EMAIL = True
for dataset_number in [1, 4, 8]:
reader = Reader()
reader.read_testing_data(dataset_number, 30000)
for num_dataset_to_use in [100, 1000, 10000, 15000, 20000, 25000, 30000, np.inf]:
reader.read_training_data(dataset_number, num_dataset_to_use)
print("Loaded dataset %s, %s vectors" % (dataset_number, num_dataset_to_use))
start = time.time()
trainingset_size = len(reader.training_attributes)
testingset_size = len(reader.testing_attributes)
dataset_size = trainingset_size + testingset_size
feature_vector_length = len(reader.training_attributes[0])
############ Perform Training -- Random Forest
# define random forest object
