# -*- coding: utf-8 -*-
"""
Source : https://towardsdatascience.com/tflearn-soving-xor-with-a-2x2x1-feed-forward-neural-network-6c07d88689ed
"""
from tflearn import DNN
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
#Training examples
X = [[0,0], [0,1], [1,0], [1,1]]
Y = [[0], [1], [1], [0]]
input_layer = input_data(shape=[None, 2]) #input layer of size 2
hidden_layer = fully_connected(input_layer , 2, activation='tanh') #hidden layer of size 2
output_layer = fully_connected(hidden_layer, 1, activation='tanh') #output layer of size 1
#use Stohastic Gradient Descent and Binary Crossentropy as loss function
regression = regression(output_layer , optimizer='sgd', loss='binary_crossentropy', learning_rate=5)
model = DNN(regression)
#fit the model
model.fit(X, Y, n_epoch=5000, show_metric=True);
#predict all examples
print ('Expected: ', [i[0] > 0 for i in Y])
print ('Predicted: ', [i[0] > 0 for i in model.predict(X)])
model.get_weights(hidden_layer.W)
model.get_weights(output_layer.W)
model.save("tflearn-xor")
from tflearn import DNN
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
# Defining all the input data X and Y List
X = [[0, 0], [0, 1], [1, 0], [1, 1]]
Y = [[0], [1], [1], [1]]
# Defining the Model or Neural Network
input_layer = input_data(shape=[None, 2])
hidden_layer = fully_connected(input_layer, 2, activation='tanh')
output_layer = fully_connected(hidden_layer, 1, activation='tanh')
# Regressor that will perform backpropogation and train our network
regression = regression(output_layer,
optimizer='sgd',
loss='binary_crossentropy',
learning_rate=5)
model = DNN(regression)
# And finally Training of the model
model.fit(X, Y, n_epoch=5000, show_metric=True)
# Model Insights
print(model.get_weights(hidden_layer.W), model.get_weights(hidden_layer.b))
print(model.get_weights(output_layer.W), model.get_weights(output_layer.b))
# Prediction examples
print('Expected: ', [i[0] > 0 for i in Y])
print('Predicted: ', [i[0] > 0 for i in model.predict(X)])
def tflearn_OneClass_NN_linear(data_train, data_test, labels_train):
X = data_train
Y = labels_train
D = X.shape[1]
No_of_inputNodes = X.shape[1]
# Clear all the graph variables created in previous run and start fresh
tf.reset_default_graph()
# Define the network
input_layer = input_data(shape=[None,
No_of_inputNodes]) # input layer of size
np.random.seed(42)
theta0 = np.random.normal(0, 1, K + K * D + 1) * 0.0001
#theta0 = np.random.normal(0, 1, K + K*D + 1) # For linear
hidden_layer = fully_connected(
input_layer,
4,
bias=False,
activation='linear',
name="hiddenLayer_Weights",
weights_init="normal") # hidden layer of size 2
output_layer = fully_connected(
hidden_layer,
1,
bias=False,
activation='linear',
name="outputLayer_Weights",
weights_init="normal") # output layer of size 1
# Initialize rho
value = 0.01
init = tf.constant_initializer(value)
rho = va.variable(name='rho', dtype=tf.float32, shape=[], initializer=init)
rcomputed = []
auc = []
sess = tf.Session()
sess.run(tf.initialize_all_variables())
# print sess.run(tflearn.get_training_mode()) #False
tflearn.is_training(True, session=sess)
print sess.run(tflearn.get_training_mode()) #now True
temp = theta0[-1]
oneClassNN_Net = oneClassNN(output_layer,
v,
rho,
hidden_layer,
output_layer,
optimizer='sgd',
loss='OneClassNN_Loss',
learning_rate=1)
model = DNN(oneClassNN_Net, tensorboard_verbose=3)
model.set_weights(output_layer.W, theta0[0:K][:, np.newaxis])
model.set_weights(hidden_layer.W, np.reshape(theta0[K:K + K * D], (D, K)))
iterStep = 0
while (iterStep < 100):
print "Running Iteration :", iterStep
# Call the cost function
y_pred = model.predict(data_train) # Apply some ops
tflearn.is_training(False, session=sess)
y_pred_test = model.predict(data_test) # Apply some ops
tflearn.is_training(True, session=sess)
value = np.percentile(y_pred, v * 100)
tflearn.variables.set_value(rho, value, session=sess)
rStar = rho
model.fit(X, Y, n_epoch=2, show_metric=True, batch_size=100)
iterStep = iterStep + 1
rcomputed.append(rho)
temp = tflearn.variables.get_value(rho, session=sess)
# print "Rho",temp
# print "y_pred",y_pred
# print "y_predTest", y_pred_test
# g = lambda x: x
g = lambda x: 1 / (1 + tf.exp(-x))
def nnScore(X, w, V, g):
return tf.matmul(g((tf.matmul(X, w))), V)
# Format the datatype to suite the computation of nnscore
X = X.astype(np.float32)
X_test = data_test
X_test = X_test.astype(np.float32)
# assign the learnt weights
# wStar = hidden_layer.W
# VStar = output_layer.W
# Get weights values of fc2
wStar = model.get_weights(hidden_layer.W)
VStar = model.get_weights(output_layer.W)
# print "Hideen",wStar
# print VStar
train = nnScore(X, wStar, VStar, g)
test = nnScore(X_test, wStar, VStar, g)
# Access the value inside the train and test for plotting
# Create a new session and run the example
# sess = tf.Session()
# sess.run(tf.initialize_all_variables())
arrayTrain = train.eval(session=sess)
arrayTest = test.eval(session=sess)
# print "Train Array:",arrayTrain
# print "Test Array:",arrayTest
# plt.hist(arrayTrain-temp, bins = 25,label='Normal');
# plt.hist(arrayTest-temp, bins = 25, label='Anomalies');
# plt.legend(loc='upper right')
# plt.title('r = %1.6f- Sigmoid Activation ' % temp)
# plt.show()
pos_decisionScore = arrayTrain - temp
neg_decisionScore = arrayTest - temp
return [pos_decisionScore, neg_decisionScore]
g = lambda x: 1 / (1 + tf.exp(-x))
def nnScore(X, w, V, g):
return tf.matmul(g((tf.matmul(X, w))), V)
# Format the datatype to suite the computation of nnscore
X = X.astype(np.float32)
X_test = data_test
X_test = X_test.astype(np.float32)
# assign the learnt weights
# wStar = hidden_layer.W
# VStar = output_layer.W
# Get weights values of fc2
wStar = model.get_weights(hidden_layer.W)
VStar = model.get_weights(output_layer.W)
print "Hideen", wStar
print VStar
train = nnScore(X, wStar, VStar, g)
test = nnScore(X_test, wStar, VStar, g)
# Access the value inside the train and test for plotting
# Create a new session and run the example
# sess = tf.Session()
# sess.run(tf.initialize_all_variables())
arrayTrain = train.eval(session=sess)
arrayTest = test.eval(session=sess)
