def test_easy_multidim_y(self):
x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0, 1], [1, 0], [0, 1], [1, 0]])
mlp = MultilayerPerceptron(
num_inputs=3, num_outputs=2,
num_hidden_layers=1, num_hidden_nodes=6, seed=323440,
learn_rate = .8, learn_rate_evol='constant', momentum=.1
)
mlp.fit(x, y, epochnum=50)
results = mlp.classify(x, max_ind=True)
assert np.allclose(to_dummies(results), y)
def test_easy_multidim_y(self):
x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0, 1], [1, 0], [0, 1], [1, 0]])
mlp = MultilayerPerceptron(num_inputs=3,
num_outputs=2,
num_hidden_layers=1,
num_hidden_nodes=6,
seed=323440,
learn_rate=.8,
learn_rate_evol='constant',
momentum=.1)
mlp.fit(x, y, epochnum=50)
results = mlp.classify(x, max_ind=True)
assert np.allclose(to_dummies(results), y)
from matplotlib import cm
# from matplotlib.backends.backend_pdf import PdfPages
# %cd C:/Users/g1rxf01/Downloads/New folder/simpleml/examples
# %cd M:/Libraries/Documents/Code/Python/simpleml/examples
sys.path.insert(1, os.path.join(sys.path[0], '..'))
from simpleml.neural import MultilayerPerceptron
from simpleml.transform import to_dummies
# Load data
with gzip.open('../data/mnist.gz', 'rb') as f:
data = pickle.load(f)
data = {key: (val[0], val[1], to_dummies(val[1])) for key, val in data.items()}
# Setup estimator
num_hidden_nodes = [101]
mlp = MultilayerPerceptron(
num_inputs=data['train'][0].shape[1]+1,
num_outputs=data['train'][2].shape[1],
num_hidden_layers=len(num_hidden_nodes), num_hidden_nodes=num_hidden_nodes,
learn_rate=.5, momentum=.1, seed=23456
)
# Estimate multilayer perceptron
start = time.perf_counter()
mlp.fit(data['train'][0], data['train'][2],
epochnum=10, verbose=1)
def test_to_dummies_nan(self):
tf.to_dummies(np.array([1, 3, 3, 1, np.nan]))
def test_to_dummies_all_unique(self):
tf.to_dummies(np.array([1, 2, 3, 4]))
def test_to_dummies(self):
assert np.allclose(tf.to_dummies(self.original), self.dummied)
assert np.allclose(tf.to_dummies(self.replaced), self.dummied)
import matplotlib.pyplot as plt
from matplotlib import cm
# from matplotlib.backends.backend_pdf import PdfPages
# %cd C:/Users/g1rxf01/Downloads/New folder/simpleml/examples
# %cd M:/Libraries/Documents/Code/Python/simpleml/examples
sys.path.insert(1, os.path.join(sys.path[0], '..'))
from simpleml.neural import MultilayerPerceptron
from simpleml.transform import to_dummies
# Load data
with gzip.open('../data/mnist.gz', 'rb') as f:
data = pickle.load(f)
data = {key: (val[0], val[1], to_dummies(val[1])) for key, val in data.items()}
# Setup estimator
num_hidden_nodes = [101]
mlp = MultilayerPerceptron(num_inputs=data['train'][0].shape[1] + 1,
num_outputs=data['train'][2].shape[1],
num_hidden_layers=len(num_hidden_nodes),
num_hidden_nodes=num_hidden_nodes,
learn_rate=.5,
momentum=.1,
seed=23456)
# Estimate multilayer perceptron
start = time.perf_counter()
mlp.fit(data['train'][0], data['train'][2], epochnum=10, verbose=1)
pred = mlp.classify(data['test'][0], max_ind=True)
def test_to_dummies_nan(self):
tf.to_dummies(np.array([1, 3, 3, 1, np.nan]))
def test_to_dummies_all_unique(self):
tf.to_dummies(np.array([1, 2, 3, 4]))
def test_to_dummies(self):
assert np.allclose(tf.to_dummies(self.original), self.dummied)
assert np.allclose(tf.to_dummies(self.replaced), self.dummied)
