def test_predict(self, ):
target = NeuralNetwork()
target.addLayer(10, 10, lambda x: x**2)
prediction = target.predict(
tf.constant([1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
dtype=tf.dtypes.float32), [tf.zeros([10, 10])],
[tf.zeros([10])])
tf.debugging.assert_equal(prediction, tf.zeros(10))
def predict(self, data):
nn = NeuralNetwork()
l1 = Layer(56, 54)
l2 = Layer(54, 25)
nn.add(l1)
nn.add(ActivationLayer(relu, relu_derivative))
nn.add(l2)
nn.add(ActivationLayer(sigmoid, sigmoid_derivative))
l1.weights = np.load('weights1.npy')
l2.weights = np.load('weights2.npy')
l1.bias = np.load('bias1.npy')
l2.bias = np.load('bias2.npy')
out = nn.predict(data)
pred = np.argmax(out)
return pred
for f in files:
d = np.loadtxt(path + "/" + f)
x.append(d)
y_name = f.split("_")[0]
y.append(wordList[y_name])
# print np.array(x), np.array(y)
x_data = np.array(x)
y_data = np.array(y)
l = LabelBinarizer()
y_data = l.fit_transform(y_data)
result = l.classes_
pickle.dump(result, open('result.pkl', 'wb'))
# x_train, x_test, y_train, y_test = train_test_split(x_data, y_data)
# labels_train = LabelBinarizer().fit_transform(y_train)
# labels_test = LabelBinarizer().fit_transform(y_test)
# print labels_test
nn = NeuralNetwork([960, 1500, 3], "logistic")
print "start"
nn.fit(x_data, y_data, epochs=1000)
pickle.dump(nn, open('nn.pkl', 'wb'))
predictions = []
for i in range(x_data.shape[0]):
o = nn.predict(x_data[i])
d = result[np.argmax(o)]
predictions.append(d)
for i in predictions:
print i
for f in files:
d = np.loadtxt(path + "/" + f)
x.append(d)
y_name = f.split("_")[0]
y.append(wordList[y_name])
# print np.array(x), np.array(y)
x_data = np.array(x)
y_data = np.array(y)
l = LabelBinarizer()
y_data = l.fit_transform(y_data)
result = l.classes_
pickle.dump(result, open('result.pkl', 'wb'))
# x_train, x_test, y_train, y_test = train_test_split(x_data, y_data)
# labels_train = LabelBinarizer().fit_transform(y_train)
# labels_test = LabelBinarizer().fit_transform(y_test)
# print labels_test
nn = NeuralNetwork([960, 1500, 3], "logistic")
print "start"
nn.fit(x_data, y_data, epochs=1000)
pickle.dump(nn, open('nn.pkl', 'wb'))
predictions = []
for i in range(x_data.shape[0]):
o = nn.predict(x_data[i])
d = result[np.argmax(o)]
predictions.append(d)
for i in predictions:
print i
import numpy as np from sklearn.datasets import load_digits from sklearn.metrics import confusion_matrix, classification_report from sklearn.preprocessing import LabelBinarizer from sklearn.model_selection import train_test_split from neuralNetwork import NeuralNetwork digits = load_digits() X = digits.data y = digits.target # preprocessing X -= X.min() X /= X.max() X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1) y_train = LabelBinarizer().fit_transform(y_train) nn = NeuralNetwork([64, 100, 10], 'logistic') nn.fit(X_train, y_train, epochs=3000) predictions = nn.predict(X_test) predictions = np.array(predictions) print(confusion_matrix(y_test, predictions)) print(classification_report(y_test, predictions))
theta = np.block([t.reshape(t.size, order='F') for t in Theta])
sample_nn = NeuralNetwork(X_s, y_s, Theta, sample_dims)
grads = sample_nn.cost_grad(theta)
n_grads = sample_nn.cost_grad_numerical(theta)
print('----------------------')
print('Analytical | Numerical')
print('----------------------')
for g, n_g in zip(grads, n_grads):
print(' {0: .4f} | {1: .4f}'.format(g, n_g))
diff = np.linalg.norm(n_grads - grads) / np.linalg.norm(n_grads + grads)
print('Relative difference: {0}'.format(diff))
print('========== Part 2.4: Regularized Neural Networks ==========')
Theta = [data['Theta1'], data['Theta2']]
theta = np.block([t.reshape(t.size, order='F') for t in Theta])
nn.update_lambda(3)
J = nn.cost(theta)
print('Regularized cost: {0:0.6f} (expected: 0.576051)'.format(J))
print('========== Part 2.5: Learning Parameters ==========')
Theta = [
nn.initialize_weights(layer_dims[i], layer_dims[i + 1], 0.12)
for i in range(len(layer_dims) - 1)
]
theta = np.block([t.reshape(t.size, order='F') for t in Theta])
nn.update_lambda(1)
J, theta = nn.optimize(theta)
p = nn.predict(theta)
print('Trained accuracy: {}'.format(np.mean(p == y) * 100))
import numpy as np
from neuralNetwork import NeuralNetwork
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn import datasets
print('[INFO] Load MNIST dataset')
digits = datasets.load_digits()
data = digits.data.astype('float')
data = (data - data.min()) / (data.max() - data.min())
print('[INFO] Sample {}, dim {}'.format(data.shape[0], data.shape[1]))
#split train, test set
X_train, X_test, y_train, y_test = train_test_split(data,
digits.target,
test_size=0.25)
#convert label one-hot encoding
y_train = LabelBinarizer().fit_transform(y_train)
y_test = LabelBinarizer().fit_transform(y_test)
print('[INFO] Train neural network')
nn = NeuralNetwork([X_train.shape[1], 32, 16, 10])
print('[INFO] Neural network info {}'.format(nn))
nn.fit(X_train, y_train, epochs=1000)
print('[INFO] Evaluate')
predict = nn.predict(X_test)
predict = np.argmax(predict, axis=1)
print(classification_report(np.argmax(y_test, axis=1), predict))
def train_net_predict_energy(L=10, N=5000):
ising = Ising(L, N)
X, y = ising.generateTrainingData1D()
y /= L
n_samples, n_features = X.shape
nn = NeuralNetwork(inputs=L,
neurons=L * L,
outputs=1,
activations='sigmoid',
cost='mse',
silent=False)
nn.addLayer(neurons=L * L)
nn.addLayer(neurons=L * L)
nn.addOutputLayer(activations='identity')
validation_skip = 10
epochs = 1000
nn.fit(X.T,
y,
shuffle=True,
batch_size=1000,
validation_fraction=0.2,
learning_rate=0.001,
verbose=False,
silent=False,
epochs=epochs,
validation_skip=validation_skip,
optimizer='adam')
# Use the net to predict the energies for the validation set.
x_validation = nn.x_validation
y_validation = nn.predict(x_validation)
target_validation = nn.target_validation
# Sort the targets for better visualization of the network output.
ind = np.argsort(target_validation)
y_validation = np.squeeze(y_validation.T[ind])
target_validation = np.squeeze(target_validation.T[ind])
# We dont want to plot the discontinuities in the target.
target_validation[np.where(
np.abs(np.diff(target_validation)) > 1e-5)] = np.nan
plt.rc('text', usetex=True)
plt.figure()
plt.plot(target_validation, 'k--', label=r'Target')
plt.plot(y_validation, 'r.', markersize=0.5, label=r'NN output')
plt.legend(fontsize=10)
plt.xlabel(r'Validation sample', fontsize=10)
plt.ylabel(r'$E / L$', fontsize=10)
#plt.savefig(os.path.join(os.path.dirname(__file__), 'figures', 'nn_1d_energy_predict' + str(L) + '.png'), transparent=True, bbox_inches='tight')
# Plot the training / validation loss during training.
training_loss = nn.training_loss
validation_loss = nn.validation_loss
# There are more training loss values than validation loss values, lets
# align them so the plot makes sense.
xaxis_validation_loss = np.zeros_like(validation_loss)
xaxis_validation_loss[0] = 0
xaxis_validation_loss[1:-1] = np.arange(validation_skip,
len(training_loss),
validation_skip)
xaxis_validation_loss[-1] = len(training_loss)
plt.figure()
plt.semilogy(training_loss, 'r-', label=r'Training loss')
plt.semilogy(xaxis_validation_loss,
validation_loss,
'k--',
label=r'Validation loss')
plt.legend(fontsize=10)
plt.xlabel(r'Epoch', fontsize=10)
plt.ylabel(r'Cost $C(\theta)$', fontsize=10)
#plt.savefig(os.path.join(os.path.dirname(__file__), 'figures', 'nn_1d_loss' + str(L) + '.png'), transparent=True, bbox_inches='tight')
plt.show()
