import numpy as np
import pandas as pd
from aibrite.ml.neuralnet import NeuralNet
from aibrite.ml.neuralnetwithmomentum import NeuralNetWithMomentum
from aibrite.ml.neuralnetwithrmsprop import NeuralNetWithRMSprop
from aibrite.ml.neuralnetwithadam import NeuralNetWithAdam
df = pd.read_csv("./data/ex2data1.csv")
train_set, dev_set, test_set = NeuralNet.split(df.values, 0.8, 0.1, 0.1)
train_x, train_y = train_set[:, 0:-1], train_set[:, -1]
dev_x, dev_y = dev_set[:, 0:-1], dev_set[:, -1]
test_x, test_y = test_set[:, 0:-1], test_set[:, -1]
nn = NeuralNet(train_x, train_y, hidden_layers=(2, 2), iteration_count=6000)
train_result = nn.train(lambda nn, iter: print("{0:.2f}".format(iter.cost))
if iter.total_iteration_index % 100 == 0 else None)
result = nn.predict(test_x, expected=test_y)
print("{0}:\n{1}\n".format(nn, NeuralNet.format_score(result.score)))
from aibrite.ml.neuralnet import NeuralNet
from aibrite.ml.neuralnetwithmomentum import NeuralNetWithMomentum
from aibrite.ml.neuralnetwithrmsprop import NeuralNetWithRMSprop
from aibrite.ml.neuralnetwithadam import NeuralNetWithAdam
from aibrite.ml.analyser import NeuralNetAnalyser
from aibrite.ml.loggers import CsvLogger
df = pd.read_csv("./data/winequality-red.csv", sep=";")
np.random.seed(5)
data = df.values
train_set, test_set, dev_set = NeuralNet.split(data,
0.6,
0.20,
0.20,
shuffle=True)
train_x, train_y = (train_set[:, 0:-1]), train_set[:, -1]
dev_x, dev_y = (dev_set[:, 0:-1]), dev_set[:, -1]
test_x, test_y = (test_set[:, 0:-1]), test_set[:, -1]
labels = [3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
normalize_inputs = [True, False]
iteration_count = [50, 100, 150]
learning_rate = [0.005, 0.002]
hidden_layers = [(32, 64, 128), (4, 4)]
lambds = [0.4, 0.8, 0.9]
learnin_rate_decay = [0.]
from aibrite.ml.neuralnet import NeuralNet
from aibrite.ml.neuralnetwithmomentum import NeuralNetWithMomentum
from aibrite.ml.neuralnetwithrmsprop import NeuralNetWithRMSprop
from aibrite.ml.neuralnetwithadam import NeuralNetWithAdam
from aibrite.ml.analyser import NeuralNetAnalyser
df = pd.read_csv("./data/ex2data1.csv", sep=",")
# df = df[df['quality'] != 8.0]
# df = df[df['quality'] != 3.0]
np.random.seed(5)
data = df.values
data = NeuralNet.shuffle(data)
train_set, test_set, dev_set = NeuralNet.split(data, 0.7, 0.15, 0.15)
train_x, train_y = train_set[:, 0:-1], train_set[:, -1]
train_x, train_y = (train_set[:, 0:-1]), train_set[:, -1]
dev_x, dev_y = (dev_set[:, 0:-1]), dev_set[:, -1]
test_x, test_y = (test_set[:, 0:-1]), test_set[:, -1]
labels = [3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
iterations = [200]
learning_rates = [0.02]
hidden_layers = [(4, 6, 12)]
lambds = [0.8]
test_sets = {
'dev': (dev_x, dev_y),
'test': (test_x, test_y),
from aibrite.ml.neuralnetwithrmsprop import NeuralNetWithRMSprop
from aibrite.ml.neuralnetwithadam import NeuralNetWithAdam
df = pd.read_csv("./data/winequality-red.csv", sep=";")
# df = df[df['quality'] != 8.0]
# df = df[df['quality'] != 3.0]
# print(df.values.shape)
# np.random.seed(1)
data = df.values
# data = NeuralNet.shuffle(data)
train_set, dev_set, test_set = NeuralNet.split(data, 0.6, 0.2, 0.2)
train_x, train_y = train_set[:, 0:-1], train_set[:, -1]
train_x, train_y = NeuralNet.zscore(train_set[:, 0:-1]), train_set[:, -1]
dev_x, dev_y = NeuralNet.zscore(dev_set[:, 0:-1]), dev_set[:, -1]
test_x, test_y = NeuralNet.zscore(test_set[:, 0:-1]), test_set[:, -1]
labels = [3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
iterations = [2000]
learning_rates = [0.008]
hidden_layers = [(24, 36, 24, 12, 6)]
test_sets = {
'dev': (dev_x, dev_y),
'test': (test_x, test_y),
'train': (train_x, train_y)
import numpy as np
import pandas as pd
from aibrite.ml.neuralnet import NeuralNet
from aibrite.ml.neuralnetwithmomentum import NeuralNetWithMomentum
from aibrite.ml.neuralnetwithrmsprop import NeuralNetWithRMSprop
from aibrite.ml.neuralnetwithadam import NeuralNetWithAdam
df = pd.read_csv("./data/winequality-red.csv", sep=";")
train_set, dev_set, test_set = NeuralNet.split(df.values,
0.8,
0.1,
0.1,
shuffle=True)
train_x, train_y = train_set[:, 0:-1], train_set[:, -1]
dev_x, dev_y = dev_set[:, 0:-1], dev_set[:, -1]
test_x, test_y = test_set[:, 0:-1], test_set[:, -1]
nn = NeuralNetWithAdam(train_x,
train_y,
hidden_layers=(32, 64, 128, 64),
iteration_count=500,
learning_rate=0.005,
epochs=1,
shuffle=True,
normalize_inputs=True)
train_result = nn.train()