def train_single(self, input_vector, target_vector):
"""
input_vector and target_vector can be tuple,
list or ndarray
"""
input_vector = np.array(input_vector, ndmin=2).T
target_vector = np.array(target_vector, ndmin=2).T
output_vector1 = np.dot(self.weights_in_hidden, input_vector)
output_hidden = Activation.reLU(output_vector1)
output_vector2 = np.dot(self.weights_hidden_output, output_hidden)
#output_network = Activation.sigmoid(output_vector2)
output_network = Activation.reLU(output_vector2)
output_errors = target_vector - output_network
# update the weights:
#tmp = output_errors * Derivative.sigmoid(output_network)
tmp = output_errors * Derivative.reLU(output_network)
tmp = self.learning_rate * np.dot(tmp, output_hidden.T)
self.weights_hidden_output += tmp
# calculate hidden errors:
hidden_errors = np.dot(self.weights_hidden_output.T, output_errors)
# ----------------------------------------------------------------------
# update the weights:
tmp = hidden_errors * Derivative.reLU(output_hidden)
# -----------------------------------------------------------------------
self.weights_in_hidden += self.learning_rate * np.dot(
tmp, input_vector.T)
def predict(self, input_vector):
# input_vector can be tuple, list or ndarray
input_vector = np.array(input_vector, ndmin=2).T
# 1st layer
output_vector = np.dot(self.weights_in_hidden, input_vector)
output_vector = Activation.reLU(output_vector)
# 2nd layer
output_vector = np.dot(self.weights_hidden_output, output_vector)
#output_vector = Activation.sigmoid(output_vector)
output_vector = Activation.reLU(output_vector)
return output_vector
def train_single(self, input_vector, target_vector):
"""
input_vector and target_vector can be tuple,
list or ndarray
"""
input_vector = np.array(input_vector, ndmin=2).T
target_vector = np.array(target_vector, ndmin=2).T
output_vector1 = np.dot(self.weights_in_hidden, input_vector)
output_hidden = Activation.reLU(output_vector1)
output_hidden *= Dropout.get_mask(output_vector1)
output_vector2 = np.dot(self.weights_hidden_output, output_hidden)
output_network = Activation.reLU(output_vector2)
output_network *= Dropout.get_mask(output_vector2)
output_errors = target_vector - output_network
# update the weights:
#tmp = output_errors * Derivative.sigmoid(output_network)
try:
tmp = output_errors * Derivative.reLU(output_network)
tmp = self.learning_rate * np.dot(tmp, output_hidden.T)
self.weights_hidden_output += tmp
except:
print("Something went wrong when writing to the file")
# calculate hidden errors:
try:
hidden_errors = np.dot(self.weights_hidden_output.T, output_errors)
except:
print("Something went wrong when writing to the file")
# ----------------------------------------------------------------------
# update the weights:
tmp1 = Derivative.reLU(output_hidden)
tmp = hidden_errors * tmp1
# -----------------------------------------------------------------------
self.weights_in_hidden += self.learning_rate * np.dot(
tmp, input_vector.T)
