def create_series(in_array,
window_size,
period,
minV,
maxV,
layer_nodes=[2, 3],
sigmoid='tanh',
epochs=50000):
global_max = maxV
global_min = minV
X_train = []
y_train = []
for i in range(len(in_array) - window_size):
X = []
for j in range(window_size):
X.append(_scale_to_binary(in_array[i + j], global_min, global_max))
X_train.append(X)
y_train.append(
_scale_to_binary(in_array[i + window_size], global_min,
global_max))
X_train = np.array(X_train)
y_train = np.array(y_train)
layers = []
layers.append(window_size)
for i in range(len(layer_nodes)):
layers.append(layer_nodes[i])
n = NeuralNetwork(layers, sigmoid)
n.fit(X_train, y_train, epochs)
X_test = in_array[-window_size:]
for i in range(len(X_test)):
X_test[i] = _scale_to_binary(X_test[i], global_min, global_max)
preds = []
X_test = deque(X_test)
for i in range(period):
val = n.predict(X_test)
preds.append(rescale_from_binary(val[0], global_min, global_max))
X_test.rotate(-1)
X_test[window_size - 1] = val[0]
return preds
def create_series(in_array,window_size,period, minV, maxV, layer_nodes = [2,3], sigmoid = 'tanh', epochs = 50000):
global_max = maxV
global_min = minV
X_train = []
y_train = []
for i in range(len(in_array)-window_size):
X = []
for j in range(window_size):
X.append(_scale_to_binary(in_array[i+j],global_min,global_max))
X_train.append(X)
y_train.append(_scale_to_binary(in_array[i+window_size],global_min,global_max))
X_train = np.array(X_train)
y_train = np.array(y_train)
layers = []
layers.append(window_size)
for i in range(len(layer_nodes)):
layers.append(layer_nodes[i])
n = NeuralNetwork(layers,sigmoid)
n.fit(X_train,y_train, epochs)
X_test = in_array[-window_size:]
for i in range(len(X_test)):
X_test[i]=_scale_to_binary(X_test[i],global_min,global_max)
preds = []
X_test = deque(X_test)
for i in range(period):
val = n.predict(X_test)
preds.append(rescale_from_binary(val[0], global_min, global_max))
X_test.rotate(-1)
X_test[window_size-1] = val[0]
return preds
X = digits.data
Y = digits.target
Y_classes = np.zeros((X.shape[0], 10))
for i in range(Y.shape[0]):
Y_classes[i, Y[i]] = 1
Y = Y_classes
X_train, X_test, y_train, y_test = train_test_split(X, Y)
nn = NeuralNetwork(X_train, y_train, X_train.shape[1], 0.01, 0.1, 1000, 100,
100, y_train.shape[1])
# nn.train_neural_network()
# Save theta values.
# nn.save_theta()
# This is executed once we have trained the neural network.
index = random.randrange(0, X_test.shape[0])
nn.load_theta('theta0.csv', 'theta1.csv', 'theta2.csv')
prediction = np.argmax(nn.predict(X_test[index, :].reshape((-1, 1))))
label = np.argmax(y_test[index])
plt.gray()
plt.matshow(X_test[index, :].reshape((8, 8)))
plt.xlabel("Prediction: " + str(prediction) + " Label: " + str(label))
plt.show()
X_train, y_train = generate_halfmoon_dataset(noise=0.1)
X_test, y_test = generate_halfmoon_dataset(noise=0.1)
nn = NeuralNetwork([2, 4, 2, 1], 0.03)
if (not os.path.isfile("nn_halfmoon_noise_0.1_tanh.npy")):
train = [X_train, y_train]
nn.train_network(train, n_epochs=0, threshold=0.001)
np.save("nn_halfmoon_noise_0.1_tanh", nn.get_network())
else:
W = np.load("nn_halfmoon_noise_0.1_tanh.npy")
print("loaded weight matrix W = %s\n" % (W))
nn.load_network(W)
y_test_test = []
for i in range(len(y_test)):
y_test_test.append(np.around(np.squeeze(nn.predict(X_test[i]))))
y_train_test = []
for j in range(len(y_test)):
y_train_test.append(np.around(np.squeeze(nn.predict(X_train[j]))))
plt.subplot(221)
plt.title("Train Data")
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_train, s=40)
plt.subplot(222)
plt.title("Testing Training Data")
plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train_test, s=40)
plt.subplot(223)
plt.title("Test Data")
