Created on
@author: fame
"""
from load_mnist import *
import hw1_linear as mlBasics
import numpy as np
# Read in training and test data
X_train, y_train = load_mnist('training', [0, 1])
X_train = np.reshape(X_train, (X_train.shape[0], -1))
X_train = np.divide(X_train, 256)
X_test, y_test = load_mnist('training', [0, 1])
X_test = np.reshape(X_test, (X_test.shape[0], -1))
X_test = np.divide(X_test, 256)
# Starting values for weights W and bias b
W0 = np.zeros(X_train.shape[1])
b0 = 0
# Optimization
num_iters = 100
eta = 0.001
pdb.set_trace()
W, b = mlBasics.train(X_train, y_train, W0, b0, num_iters, eta)
# Test on test data
yhat = mlBasics.predict(X_test, W, b) >= .5
print(np.mean(yhat == y_test) * 100, "of test examples classified correctly.")
from load_mnist import *
import hw1_linear as mlBasics
import numpy as np
import matplotlib.pyplot as plt
# Read in training and test data
X_train, y_train = load_mnist('training', [0, 1])
X_train = np.reshape(X_train, (X_train.shape[0], -1))
X_train = np.divide(X_train, 256)
X_test, y_test = load_mnist('training', [0, 1])
X_test = np.reshape(X_test, (X_test.shape[0], -1))
X_test = np.divide(X_test, 256)
# Starting values for weights W and bias b
W0 = np.zeros(X_train.shape[1])
b0 = 0
# Optimization
num_iters = 1000
eta = 0.001
W, b, all_losses = mlBasics.train(X_train, y_train, W0, b0, num_iters, eta)
# Test on test data
yhat = mlBasics.predict(X_test, W, b) >= .5
print(
np.mean(yhat == y_test) * 100, "% of test examples classified correctly.")
#plot it
plt.plot(range(num_iters), all_losses)
plt.show()
