import pickle
from sarcos import download_sarcos
from sarcos import load_sarcos
from sarcos import nMSE
from sklearn.preprocessing import StandardScaler
from multilayer_neural_network import MultilayerNeuralNetwork
from minibatch_sgd import MiniBatchSGD
if __name__ == "__main__":
np.random.seed(0)
# Download Sarcos dataset if this is required
download_sarcos()
# Load training set and test set
X, Y = load_sarcos("train")
X_test, Y_test = load_sarcos("test")
# Scale targets
target_scaler = StandardScaler()
Y = target_scaler.fit_transform(Y)
Y_test = target_scaler.transform(Y_test)
# Train model (code for exercise 10.2 1/2/3)
############################################################################
# Train neural network
D = (X.shape[1], )
F = Y.shape[1]
layers = \
[
{
"type": "fully_connected",
]
sorted_test_error = [
error for _, error in sorted(zip(indices, test_error_list))
]
data = {
'params': sorted_param,
'train_error': sorted_train_error,
'test_error': sorted_test_error
}
df = pd.DataFrame(data=data)
df.to_csv(path, index=False)
if __name__ == "__main__":
download_sarcos()
X_train, Y_train = load_sarcos('train')
X_train, Y_train = X_train[0:30000], Y_train[0:30000]
target_scaler, feature_scaler = StandardScaler(), StandardScaler()
Y_train, X_train = target_scaler.fit_transform(
Y_train), feature_scaler.fit_transform(X_train)
layers = [{"type": "fully_connected", "num_nodes": 90}]
parameters = {
'alpha': [0.0005, 0.003, 0.01],
'alpha_decay': [0.95, 0.97, 1],
'batch_size': [30, 55, 80],
'eta': [0.2, 0.5, 0.8],
'eta_inc': [0, 0.00001]
}
scores, params = [], []
F = Y_train.shape[1]
D = (X_train.shape[1], )
from sarcos import download_sarcos
from sarcos import load_sarcos
from sarcos import nMSE
from sklearn.preprocessing import StandardScaler
from multilayer_neural_network import MultilayerNeuralNetwork
from minibatch_sgd import MiniBatchSGD
import time
import numpy as np
if __name__ == "__main__":
download_sarcos()
X_train, Y_train = load_sarcos('train')
X_test, Y_test = load_sarcos('test')
target_scaler, feature_scaler = StandardScaler(), StandardScaler()
Y_train, X_train = target_scaler.fit_transform(
Y_train), feature_scaler.fit_transform(X_train)
Y_test, X_test = target_scaler.transform(Y_test), feature_scaler.transform(
X_test)
layers = [{"type": "fully_connected", "num_nodes": 90}]
F = Y_train.shape[1]
D = (X_train.shape[1], )
model = MultilayerNeuralNetwork(D,
F,
layers,
training='regression',
std_dev=0.01,
verbose=True)
mbsgd = MiniBatchSGD(net=model,
epochs=100,