BATCH_SIZE = 100
for i in range(num_iter):
idx = np.random.choice(MNIST_NUM_TRAINING, BATCH_SIZE, replace=True)
x_batch = x_train[idx, :]
y_batch = y_train[idx]
pre_activation, hidden_output = hidden.forward_pass(x_batch, W1, b1)
hidden_layer_weights = [W1]
output_prob, loss = softmax.forward_pass(hidden_output, y_batch, W2, b2,
reg_parameter,
hidden_layer_weights)
g_W2, g_b2, g_output = softmax.backward_pass(output_prob, hidden_output,
y_batch, W2, b2,
reg_parameter)
g_W1, g_b1 = hidden.backward_pass(x_batch, W1, pre_activation, g_output,
W2, reg_parameter)
W1 = W1 - learning_rate * g_W1
b1 = b1 - learning_rate * g_b1
W2 = W2 - learning_rate * g_W2
b2 = b2 - learning_rate * g_b2
if i % 50 == 0:
print('iteration: {:3d} loss: {:3e}'.format(i, loss))
# prediction
hidden = np.tanh(np.dot(x_train, W1) + b1)
reg_parameter = 0.01 # regularization strength
softmax = SoftmaxLayer()
num_iter = 1000
BATCH_SIZE = 500
for i in range(num_iter):
idx = np.random.choice(MNIST_NUM_TRAINING, BATCH_SIZE, replace=True)
x_batch = x_train[idx, :]
y_batch = y_train[idx]
output_prob, loss = softmax.forward_pass(x_batch, y_batch, W, b, reg_parameter, [])
if i % 50 == 0:
print('iteration: {:3d} loss: {:3e}'.format(i, loss))
gradW, gradB, _ = softmax.backward_pass(output_prob, x_batch, y_batch, W, b, reg_parameter)
W = W - learning_rate * gradW
b = b - learning_rate * gradB
# prediction
pred_prob = np.dot(x_train, W) + b
predicted_class = np.argmax(pred_prob, axis=1)
print('---------------------------------------')
print('training set accuracy: {:f}'.format(np.mean(predicted_class == y_train)))
print('---------------------------------------')
print('\n')
x_test, y_test = load_mnist(MNIST_TESTING_X, MNIST_TESTING_y)
x_test = x_test.reshape(MNIST_NUM_TESTING, MNIST_NUM_FEATURES)
y_test = y_test.reshape(MNIST_NUM_TESTING)
hidden = TanhLayer()
num_iter = 12000
BATCH_SIZE = 100
for i in range(num_iter):
idx = np.random.choice(MNIST_NUM_TRAINING, BATCH_SIZE, replace=True)
x_batch = x_train[idx, :]
y_batch = y_train[idx]
pre_activation, hidden_output = hidden.forward_pass(x_batch, W1, b1)
hidden_layer_weights = [W1]
output_prob, loss = softmax.forward_pass(hidden_output, y_batch, W2, b2, reg_parameter, hidden_layer_weights)
g_W2, g_b2, g_output = softmax.backward_pass(output_prob, hidden_output, y_batch, W2, b2, reg_parameter)
g_W1, g_b1 = hidden.backward_pass(x_batch, W1, pre_activation, g_output, W2, reg_parameter)
W1 = W1 - learning_rate * g_W1
b1 = b1 - learning_rate * g_b1
W2 = W2 - learning_rate * g_W2
b2 = b2 - learning_rate * g_b2
if i % 50 == 0:
print('iteration: {:3d} loss: {:3e}'.format(i, loss))
# prediction
hidden = np.tanh(np.dot(x_train, W1) + b1)
pred_prob = np.dot(hidden, W2) + b2
predicted_class = np.argmax(pred_prob, axis=1)
