for i in range(25):
img = X_train[y_train == 7][i].reshape(28, 28)
ax[i].imshow(img, cmap='Greys')
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
plt.show()
# Save data in compressed npz file
#np.savez_compressed('mnist_scaled.npz', X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)
mnist = np.load('mnist_scaled.npz')
mnist.files
X_test, X_train, y_train, y_test = [mnist[f] for f in mnist.files]
from neuralnet import NeuralNetMLP
nn = NeuralNetMLP(n_hidden=100,
l2=0.01,
epochs=200,
eta=0.0005,
minibatch_size=100,
shuffle=True,
seed=1)
nn.fit(X_train=X_train[:55000],
y_train=y_train[:55000],
X_valid=X_train[55000:],
y_valid=y_train[55000:])
nn = NeuralNetMLP(n_output=10,
n_features= X_train.shape[1],
n_hidden = 50,
l2 = 0.1,
l1 = 0.0,
epochs=1000,
eta=0.001,
alpha=0.001,
decrease_const=0.00001,
shuffle=True,
minibatches=50,
random_state= 1)
start_time = time.time()
nn.fit(X_train,y_train, print_progress=True)
y_train_pred = nn.predict(X_train)
y_test_pred = nn.predict(X_test)
end_time = time.time()
total_time = start_time - end_time
f = open('out.txt', 'w')
for val in y_train_pred:
f.write(str(val) + '\n')
# ax[0].set_xticks([])
# ax[0].set_yticks([])
# plt.tight_layout()
# plt.show()
#
# np.savetxt('train_img.csv', X_train,fmt='%i', delimiter='.')
# np.savetxt('train_labels.csv', y_train,fmt='%i', delimiter='.')
# np.savetxt('test_img.csv', X_test,fmt='%i', delimiter='.')
# np.savetxt('test_labels.csv', y_test,fmt='%i', delimiter='.')
nn = NeuralNetMLP(n_output=10,
n_features=X_train.shape[1],
n_hidden=50,
l2=0.1,
l1=0.0,
epochs=1000,
eta=0.001,
alpha=0.001,
decrease_const=0.00001,
shuffle=True,
minibatches=50,
random_state=1)
nn.fit(X_train, y_train, print_progress=True)
plt.plot(range(len(nn.cost_)), nn_cost_)
plt.ylim([0, 2000])
plt.ylabel('Cost')
plt.xlabel('Epochs * 50')
plt.tight_layout()
plt.show()
with open(images_path, 'rb') as imgpath:
magic, num, rows, cols = struct.unpack(">IIII", imgpath.read(16))
images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784)
return images, labels
X_train, y_train = load_mnist('mnist', kind='train')
print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))
from neuralnet import NeuralNetMLP
# new neural network with 10 hidden layers, no regularization (l1, l2) = (0, 0)
# no adaotuve learning, no momentum learning and regular gradient descent
# using minibatches to 1
nn_check = NeuralNetMLP(n_output=10,
n_features=X_train.shape[1],
n_hidden=10,
l2=0.0,
l1=0.0,
epochs=10,
eta=0.001,
alpha=0.0,
decrease_const=0.0,
minibatches=1,
random_state=1)
nn_check.fit(X_train[:5], y_train[:5], print_progress=False)
#a 784-50-10 MLP
nn = NeuralNetMLP(n_output = 10,
n_features = X_train.shape[1],
n_hidden = 50,
l2 = 0.1,
l1 = 0.0,
epochs = 1000,
eta = 0.001,
alpha = 0.001,
decrease_const = 0.00001,
shuffle = True,
minibatches = 50,
random_state = 1)
nn.fit(X_train,y_train,print_progress = True)
#plot every 50th step to account for the 50 mini-batches (50 mini-batches x1000 epochs)
plt.plot(range(len(nn.cost_)),nn.cost_)
plt.ylim([0,2000])
plt.ylabel('Cost')
plt.xlabel('Epochs *50')
plt.tight_layout()
plt.show()
#plot a smoother version of the cost function against the number of epochs by averaging over the mini-batch
#intervals
batches = np.array_split(range(len(nn.cost_)),1000)
cost_ary = np.array(nn.cost_)
cost_avgs = [np.mean(cost_ary[i]) for i in batches]
ax[i].imshow(img, cmap='Greys')
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
fig.savefig('mnist_0_9.pdf')
y_train=np.argmax(y_train, axis=1)
y_test=np.argmax(y_test, axis=1)
np.savez_compressed('mnist_scaled.npz', X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)
'''
mnist = np.load('mnist_scaled.npz')
X_train, X_test, y_train, y_test = mnist['X_train'], mnist['X_test'], mnist['y_train'], mnist['y_test']
from neuralnet import NeuralNetMLP
nn = NeuralNetMLP(n_hidden=100, l2=0.01, epochs=200, eta=0.005, minibatch_size=100, shuffle=True, seed=1)
nn.fit(X_train=X_train[:50000], y_train=y_train[:50000], X_valid=X_train[50000:], y_valid=y_train[50000:])
fig = plt.figure()
plt.plot(range(nn.epochs), nn.eval_['cost'])
plt.ylabel('Cost')
plt.xlabel('Epochs')
fig.savefig('NeuralNetMLP_cost.pdf')
fig = plt.figure()
plt.plot(range(nn.epochs), nn.eval_['train_acc'], label='training')
plt.plot(range(nn.epochs), nn.eval_['valid_acc'], label='validation', linestyle='--')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
fig.savefig('NeuralNetMLP_acc.pdf')
