best_softmax = None
'''
batch_sizes = [100, 200]
learning_rates = [1e-7, 5e-7]
regularization_strengths = [5e4, 1e5]
'''
regularization_strengths = [5e4, 1e5, 5e5, 1e8]
################################################################################
# TODO: #
# Use the validation set to set the learning rate and regularization strength. #
# Save the best trained softmax classifer in best_softmax. #
# Hint: about 10 lines of code expected
################################################################################
ns=SoftmaxClassifier()
max_iters=8000
for rs in regularization_strengths:
ns.train_fmin(X_train,y_train,rs,max_iters)
ta=np.mean(y_train == ns.predict(X_train))
va=np.mean(y_val == ns.predict(X_val))
results[rs]=(ta,va)
if va>best_val:
best_val=va
best_reg = rs
best_softmax=ns
print '\t---- FINISHED fmin with reg: %e------' %rs
################################################################################
# END OF YOUR CODE #
################################################################################
"""
batch_sizes = [400]
learning_rates = [5e-6]
regularization_strengths = [1e5]
"""
batch_sizes = [100, 200, 400, 800, 1600, 3200, 6400]
learning_rates = [1e-7, 5e-7, 1e-6, 5e-6]
regularization_strengths = [5e4, 1e5, 5e5, 1e6, 5e6]
################################################################################
# TODO: #
# Use the validation set to set the learning rate and regularization strength. #
# Save the best trained softmax classifer in best_softmax. #
################################################################################
ns = SoftmaxClassifier()
max_iters = 8000
tol = 1e-4
for bs in batch_sizes:
for lr in learning_rates:
for rs in regularization_strengths:
iterations = ns.train(X_train, y_train, lr, rs, max_iters, bs, tol, verbose=True)[0]
ta = np.mean(y_train == ns.predict(X_train))
va = np.mean(y_val == ns.predict(X_val))
results[bs, lr, rs] = (iterations, ta, va)
if va > best_val:
best_val = va
best_bs = bs
best_it = iterations
best_lr = lr
best_reg = rs
