def optim():
"""Determine best lambda parameter"""
#list of lambda values to try
lambdas = [
0, 0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.64, 1.28, 2.56, 5.12, 10.24
]
#lambdas = [2.56, 5.12, 10, 20, 40, 80, 160, 320, 640, 1280, 2560]
#list to record training parameters
histories = []
J_train = []
J_test = []
accuracies = []
for l in lambdas:
#define and train model
model = NeuralNet(alpha=0.003,
n_iter=40000,
lamb=l,
hidden_size=28,
n_labels=2)
Theta1, Theta2, cost_history = model.fit(X_train, Y_train)
#append parameters to lists
histories.append(cost_history)
J_train.append(cost_history[-1])
X_test_1 = model.add_intercept(X_test)
J_test.append(model.forward_prop(X_test_1, Y_test, Theta1, Theta2)[4])
accuracies.append(model.predict(Theta1, Theta2, X_test, Y_test)[1])
#plot costs VS lambda
fig, ax = plt.subplots()
ax.plot(lambdas, J_train, color="C0", label="training")
ax.plot(lambdas, J_test, color="C2", label="testing")
ax.set_xlabel("Lambda")
ax.set_ylabel("Cost")
ax.legend()
plt.show()
#plot accuracies VS lambdas
fig, ax = plt.subplots()
ax.plot(lambdas, accuracies)
ax.set_xlabel("Lambda")
ax.set_ylabel("Accuracy on test sample")
plt.show()
return accuracies
def train():
"""Train the neural network"""
model = NeuralNet(alpha=0.003,
n_iter=300000,
lamb=5,
hidden_size=28,
n_labels=2)
start = time.time()
Theta1, Theta2, cost_history = model.fit(X, Y)
stop = time.time()
elapsed = stop - start
pred, accu = model.predict(Theta1, Theta2, X_test, Y_test)
print("Time to train: {:.1f} seconds".format(elapsed))
print("Model accuracy: {:.3f}".format(accu))
model.plot_history(cost_history)
return Theta1, Theta2, pred, accu, cost_history
from parse_json import parse_json
import sys
import json
import numpy as np
import pandas as pd
if __name__ == "__main__":
# <learning-rate> <#hidden-units> <#epochs> <train-set-file> <test-set-file>
if len(sys.argv) == 1:
learning_rate = 0.05
hidden_units = 7
num_epochs = 20
train = "datasets/heart_train.json"
test = "datasets/heart_test.json"
else:
tmp, learning_rate, hidden_units, num_epochs, train, test = sys.argv
learning_rate = float(learning_rate)
hidden_units = int(hidden_units)
num_epochs = int(num_epochs)
# parse the json file for data
X_train, y_train, meta_train = parse_json(train)
X_test, y_test, meta_test = parse_json(test)
nn = NeuralNet(learning_rate=learning_rate,
num_epoch=num_epochs,
hidden_dim=(hidden_units, ))
nn.fit(X_train, y_train, meta_train, verbose=True)
nn.predict(X_test, y_true=y_test, verbose=True)
hidden_units = 7
num_epochs = 50
train = "datasets/heart_train.json"
test = "datasets/heart_test.json"
# parse the json file for data
X_train, y_train, meta_train = parse_json(train)
X_test, y_test, meta_test = parse_json(test)
print("model,epoch,F1_train,F1_test")
# Run logistic regression
for epoch in range(1,num_epochs+1):
logistic = NeuralNet(learning_rate=learning_rate, num_epoch=epoch,hidden_dim=())
logistic.fit(X_train, y_train, meta_train)
F1_train = logistic.predict(X_train, y_true=y_train, F1=True)
F1_test = logistic.predict(X_test, y_true=y_test, F1=True)
print("logistic-0.05-heart,{},{},{}".format(epoch,F1_train,F1_test))
# Run neural network
for epoch in range(1,num_epochs+1):
nnet = NeuralNet(learning_rate=learning_rate, num_epoch=epoch,hidden_dim=(hidden_units,))
nnet.fit(X_train, y_train, meta_train)
F1_train = nnet.predict(X_train, y_true=y_train, F1=True)
F1_test = nnet.predict(X_test, y_true=y_test, F1=True)
print("nnet-0.05-7-heart,{},{},{}".format(epoch,F1_train,F1_test))