def __init__(self, in_dim, hidden_dim, out_dim, activation, loss_type, layer_num=0): NeuralNetwork.__init__(self, activation, loss_type) args = [self.activation, self.grad_activation] self.layers = [] self.layers.append(FullyConnectedLayer(in_dim, hidden_dim, *args)) for _ in xrange(layer_num): self.layers.append(FullyConnectedLayer(hidden_dim, hidden_dim, *args)) if loss_type == 'mse': self.layers.append(FullyConnectedLayer(hidden_dim, out_dim, *args)) else: from SoftmaxLayer import SoftmaxLayer self.layers.append(SoftmaxLayer(hidden_dim, out_dim, *args))
def __init__(self, input, output, output_act, eval_metric): NeuralNetwork.__init__(self, input, output, output_act, eval_metric) self.w_size = self.get_weight_vector_length() self.initialise_cache() self.W1 = np.random.randn(self.input, self.output) / np.sqrt( self.input) self.B1 = np.random.randn(1, self.output) / np.sqrt( self.output) # bias first layer self.out = np.zeros((1, self.output)) # output layer for base model self.final_out = np.zeros( (1, self.output)) # Final output for the model
def __init__(self): """--------------------------------------------------------------------- Desc.: Class Constructor Args: - Returns: - ---------------------------------------------------------------------""" NeuralNetwork.__init__(self, [28 * 28, 10 * 10, 10]) print "----------------------------------------------------------------" print "Digit Classfier using pytorch nn module" print "Author: Ankit Manerikar" print "Written on: 09-21-2017" print "----------------------------------------------------------------" print "Loading MNIST Dataset ..." self.train_images = read_image_file( './data/raw/train-images-idx3-ubyte') self.target_val = read_label_file('./data/raw/train-labels-idx1-ubyte') print "Dataset Loaded" print "\nClass initialized"
def __init__(self, X_data, Y_data, n_hidden_neurons=100, epochs=10, batch_size=100, eta=0.1, lmbd=0.0, activation_func='relu', activation_func_out='leaky_relu', cost_func='MSE', leaky_a=0.01): if len(Y_data.shape) == 1: Y_data = np.expand_dims(Y_data, 1) n_categories = Y_data.shape[1] NN.__init__(self, X_data, Y_data, n_hidden_neurons, n_categories, epochs, batch_size, eta, lmbd, activation_func, activation_func_out, cost_func)
def __init__(self, input, hidden, output, max_depth, output_act, eval_metric): self.hidden = hidden self.max_depth = max_depth NeuralNetwork.__init__(self, input, output, output_act, eval_metric) self.w_size = self.get_weight_vector_length() self.initialise_cache() # WEIGHTS FROM INPUT TO FIRST HIDDEN LAYER self.W1 = np.random.randn(self.input, self.hidden) / np.sqrt( self.input) self.B1 = np.random.randn(1, self.hidden) / np.sqrt(self.hidden) # WEIGHTS FROM LAST HIDDEN LAYER TO OUTPUT LAYER self.W2 = np.random.randn(self.hidden, self.output) / np.sqrt( self.hidden) self.B2 = np.random.randn(1, self.output) / np.sqrt(self.hidden) self.out = np.zeros((1, self.output)) # output layer for base model # NOW LETS CREATE ALL OF THE HIDDEN LAYERS self.h_weights = [] self.h_biases = [] self.h_out = [] for layer in range(self.max_depth): self.h_weights.append( np.random.randn(self.hidden, self.hidden) / np.sqrt(self.hidden)) self.h_biases.append( np.random.randn(1, self.hidden) / np.sqrt(self.hidden)) self.h_out.append(np.zeros((1, self.hidden))) self.final_out = np.zeros( (1, self.output)) # Final output for the model
def __init__(self): NeuralNetwork.__init__(self, [2, 1]) self.truth_table = [[False, False], [False, True], [True, False], [True, True]] self.target_val = [x[0] or x[1] for x in self.truth_table]
def __init__(self): NeuralNetwork.__init__(self, [1, 1]) self.truth_table = [False, True] self.target_val = [not x for x in self.truth_table]
def __init__(self, input, output, output_act, eval_metric): NeuralNetwork.__init__(self, input, output, output_act, eval_metric)
def __init__(self, layers=[]): NeuralNetwork.__init__(self, layers) for i in range(self.number_hidden_layers): self.activation_function[i] = self.ACTIVATION_FUNCTION_SIGMOID self.output_activation_function = self.ACTIVATION_FUNCTION_SIGMOID self.loss_function = self.LOSS_FUNCTION_MSE
def __init__(self): NeuralNetwork.__init__(self)