train_output = np.vstack([train_output, o])
nn = MLP(841, (100, 26),
activation_functions=[sigmoid, sigmoid],
rng=(lambda n: np.random.normal(0, 0.01, n)))
train_cost, cv_cost = \
nn.train_backprop(train_input, train_output,
d_f_list=[d_sigmoid, d_sigmoid],
goal=log_Bernoulli_likelihood,
d_goal=d_log_Bernoulli_likelihood,
batch_size=None,
max_iter=2500,
learning_rate=0.1,
momentum_rate=0.9,
neural_local_gain=(0.005, 0.995, 0.001, 1000),
stop_threshold=0.05,
cv_input_data=cv_input,
cv_output_data=cv_output,
#regularization_rate=0.1,
#regularization_norm=l2,
#d_regularization_norm=d_l2
verbose=True
)
t = np.argmax(train_output, axis=1)
y = np.argmax(nn.compute_output(train_input), axis=1)
print('%s / %s' % (sum(t == y), train_output.shape[0]))
t = np.argmax(test_output, axis=1)
train_output = np.vstack([train_output, o])
nn = MLP(841, (100, 26),
activation_functions=[tanh, softmax],
rng=(lambda n: np.random.normal(0, 0.01, n)))
train_cost, cv_cost = \
nn.train_backprop(train_input, train_output,
d_f_list=[d_tanh, d_softmax],
goal=cross_entropy,
d_goal=d_cross_entropy,
batch_size=1,
max_iter=100,
learning_rate=0.01,
momentum_rate=0.9,
#neural_local_gain=(0.0005, 0.9995, 0.001, 1000),
stop_threshold=0.05,
cv_input_data=cv_input,
cv_output_data=cv_output,
#regularization_rate=0.1,
#regularization_norm=l2,
#d_regularization_norm=d_l2,
verbose=True
)
t = np.argmax(train_output, axis=1)
y = np.argmax(nn.compute_output(train_input), axis=1)
print('%s / %s' % (sum(t == y), train_output.shape[0]))
t = np.argmax(test_output, axis=1)
train_input = np.vstack([train_input, v])
train_output = np.vstack([train_output, o])
nn = MLP(841, (100, 26),
activation_functions=[sigmoid, sigmoid],
rng=(lambda n: np.random.normal(0, 0.01, n)))
train_cost, cv_cost = \
nn.train_backprop(train_input, train_output,
d_f_list=[d_sigmoid, d_sigmoid],
goal=log_Bernoulli_likelihood,
d_goal=d_log_Bernoulli_likelihood,
batch_size=None,
max_iter=2500,
learning_rate=0.1,
momentum_rate=0.9,
neural_local_gain=(0.005, 0.995, 0.001, 1000),
stop_threshold=0.05,
cv_input_data=cv_input,
cv_output_data=cv_output,
#regularization_rate=0.1,
#regularization_norm=l2,
#d_regularization_norm=d_l2
verbose=True
)
t = np.argmax(train_output, axis=1)
y = np.argmax(nn.compute_output(train_input), axis=1)
print('%s / %s' % (sum(t == y), train_output.shape[0]))
t = np.argmax(test_output, axis=1)
df = read_csv('./../data/africa-soil/training.csv')
x = df.as_matrix(columns=df.columns[1:3595])
x[:, -1] = (x[:, -1] == 'Topsoil') * 1.0
x = x.astype(float)
y = df.as_matrix(columns=df.columns[3595:])
y = y.astype(float)
idx_train = list(
np.random.choice(range(x.shape[0]), size=int(round(0.8 * x.shape[0]))))
idx_cv = list(set(range(x.shape[0])) - set(idx_train))
nn = MLP(3594, (50, 5),
activation_functions=[tanh, identity],
rng=(lambda n: np.random.normal(0, 0.01, n)))
train_cost, cv_cost = \
nn.train_backprop(x[idx_train, :], y[idx_train, :],
d_f_list=[d_tanh, d_identity],
batch_size=None,
max_iter=1000,
learning_rate=0.001,
momentum_rate=0.9,
neural_local_gain=(0.0005, 0.9995, 0.001, 1000),
stop_threshold=0.05,
cv_input_data=x[idx_cv, :],
cv_output_data=y[idx_cv, :],
#regularization_rate=0.1,
#regularization_norm=l2,
#d_regularization_norm=d_l2
verbose=True
)
if __name__ == '__main__':
df = read_csv('./../data/africa-soil/training.csv')
x = df.as_matrix(columns=df.columns[1:3595])
x[:, -1] = (x[:, -1] == 'Topsoil') * 1.0
x = x.astype(float)
y = df.as_matrix(columns=df.columns[3595:])
y = y.astype(float)
idx_train = list(np.random.choice(range(x.shape[0]), size=int(round(0.8 * x.shape[0]))))
idx_cv = list(set(range(x.shape[0])) - set(idx_train))
nn = MLP(3594, (50, 5),
activation_functions=[tanh, identity],
rng=(lambda n: np.random.normal(0, 0.01, n)))
train_cost, cv_cost = \
nn.train_backprop(x[idx_train, :], y[idx_train, :],
d_f_list=[d_tanh, d_identity],
batch_size=None,
max_iter=1000,
learning_rate=0.001,
momentum_rate=0.9,
neural_local_gain=(0.0005, 0.9995, 0.001, 1000),
stop_threshold=0.05,
cv_input_data=x[idx_cv, :],
cv_output_data=y[idx_cv, :],
#regularization_rate=0.1,
#regularization_norm=l2,
#d_regularization_norm=d_l2
verbose=True
)
