def trainNeuralNetwork(reload=False, wantDeskew=True):
# Get the MNIST data, either by loading saved data or getting it new.
X_train, labels_train, X_test, labels_test = get_data(reload, wantDeskew)
print("X_train.shape", X_train.shape)
print("labels_train.shape", labels_train.shape)
# ___________ Initialize the neural network. ____________
neural_net = NeuralNetwork(n_in=X_train.shape[1],
n_hid=1200,
n_out=10,
eta=0.1,
decay_const=0.8,
alpha=0.9,
l2=0.07,
batch_size=50,
n_epochs=15)
# Give training data to neural_net to store and further preprocessing.
neural_net.set_data(X_train, labels_train)
# Create arrays for plotting results.
accuracy = []
loss = []
n_train = int(50e3)
n_batches = n_train // neural_net.batch_size
batches = np.array_split(np.arange(n_train), n_batches)
print("Splitting into", len(batches), "of size", neural_net.batch_size)
epochs = np.arange(neural_net.n_epochs)
n_iter_total = len(epochs) * len(batches)
x_axis = np.arange(0, n_iter_total, n_iter_total // 100)
print("preparing to collect", len(x_axis), "points to plot.")
print("Beginning", n_iter_total,
"iterations of minibatch gradient descent.")
for i in epochs:
print('\n========== EPOCH {} ======'.format(i))
neural_net.new_epoch(i)
for j, batch in enumerate(batches):
# Tell neural net what data to train with.
neural_net.set_active(batch)
# Calculate values along feedforward.
X, S_h, H, S_o, O = neural_net.forward_pass()
# Get the training loss at this iteration.
if i * len(batches) + j in x_axis:
print(".", end=" ")
sys.stdout.flush()
loss.append(neural_net.get_loss())
accuracy.append(neural_net.train_accuracy())
# Update weights via backprop.
neural_net.train(X, H, O)
# Save Kaggle predictions in CSV file.
if True:
pred_labels_test = neural_net.predict_test(util.preprocess(X_test))
Id = np.reshape(np.arange(1, 1 + X_test.shape[0]),
(X_test.shape[0], 1))
Category = np.reshape(pred_labels_test, (X_test.shape[0], 1))
columns = np.hstack((Id, Category))
np.savetxt('predictions.csv',
columns,
delimiter=',',
header='Id, Category',
fmt='%d')
neural_net.print_results()
util.plot_error(x_axis, loss, accuracy, neural_net.get_params())
