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+import z_helper as h +import time +np.set_printoptions(linewidth=200) + +data_input = np.load("data/ci_2_inputs.npy") +data_output = np.load("data/ci_2_outputs.npy") + +print(data_input.shape) +print(data_output.shape) + +print("Begin compiling!") +begin_time = time.time_ns() +compiled_nn_values = nn.make_neural_network(layer_sizes=[data_input.shape[1], data_output.shape[1]], layer_activations=[h.sigmoid]) +nn.train_auto(data_input[:10], data_output[:10], data_input[10: 15], data_output[10: 15], compiled_nn_values) +end_time = time.time_ns() +print("Compile time:", (end_time-begin_time) / 1e9) + +np.random.seed(420) +total_accuracy = 0.0 +begin_total = time.time_ns() +n = 10 +for i in range(n): + + random_seed = np.random.randint(10, 1010) + np.random.seed(random_seed) + + train_input, validate_input, test_input = h.kfold(7, data_input, random_seed) + train_output, validate_output, test_output = h.kfold(7, data_output, random_seed) + nn_values = nn.make_neural_network(layer_sizes=[train_input.shape[1], 20, train_output.shape[1]], layer_activations=[h.sigmoid, h.softmax_2]) + + begin_time = time.time_ns() + epochs, current_mse = nn.train_auto(train_input, train_output, validate_input, validate_output, nn_values) + end_time = time.time_ns() + + train_mse = nn.calculate_MSE(train_input, train_output, nn_values) + test_mse = nn.calculate_MSE(test_input, test_output, nn_values) + + accuracy_test = nn.evaluate(test_input, test_output, nn_values) + total_accuracy += accuracy_test + print("Seed:", random_seed, "Epochs:", epochs, "Time:", (end_time-begin_time)/1e9, "Accuracy:", accuracy_test, "Tr:", train_mse, "V:", current_mse, "T:", test_mse) +print("Average Accuracy:", total_accuracy / n, "Average Time:", ((time.time_ns()-begin_total)/1e9) / n) diff --git a/numpy_neural_network_2.py b/numpy_neural_network_2.py new file mode 100644 index 0000000..21c6bc2 --- /dev/null +++ b/numpy_neural_network_2.py @@ -0,0 +1,156 @@ +import numpy as np +from numba.experimental import jitclass +from numba import njit, types, typed, prange +import z_helper as h +import time + +from numba.core.errors import NumbaTypeSafetyWarning +import warnings + +warnings.simplefilter('ignore', category=NumbaTypeSafetyWarning) + +# spec = [ +# ("layer_sizes", types.ListType(types.int64)), +# ("layer_activations", types.ListType(types.FunctionType(types.float64[:, ::1](types.float64[:, ::1], types.boolean)))), +# ("weights", types.ListType(types.float64[:, ::1])), +# ("biases", types.ListType(types.float64[:, ::1])), +# ("layer_outputs", types.ListType(types.float64[:, ::1])), +# ("learning_rate", types.float64), +# ] +# @jitclass(spec) + + +class NeuralNetwork: + def __init__(self, layer_sizes, layer_activations, weights, biases, layer_outputs, learning_rate): + self.layer_sizes = layer_sizes + self.layer_activations = layer_activations + self.weights = weights + self.biases = biases + self.layer_outputs = layer_outputs + self.learning_rate = learning_rate + + +def make_neural_network(layer_sizes, layer_activations, learning_rate=0.05, low=-2, high=2): + for size in layer_sizes: + assert size > 0 + + # Initialize typed layer sizes list. + # typed_layer_sizes = typed.List() + # for size in layer_sizes: + # typed_layer_sizes.append(size) + # print(typeof(typed_layer_sizes)) + typed_layer_sizes = layer_sizes + + # Initialie typed layer activation method strings list. + # prototype = types.FunctionType(types.float64[:, ::1](types.float64[:, ::1], types.boolean)) + # typed_layer_activations = typed.List.empty_list(prototype) + # for activation in layer_activations: + # typed_layer_activations.append(activation) + # print(typedof(typed_layer_activations)) + typed_layer_activations = layer_activations + + # Initialize weights between every neuron in all adjacent layers. + # typed_weights = typed.List() + # for i in range(1, len(layer_sizes)): + # typed_weights.append(np.random.uniform(low, high, (layer_sizes[i-1], layer_sizes[i]))) + # print(typeof(typed_weights)) + typed_weights = [np.random.uniform(low, high, (layer_sizes[i-1], layer_sizes[i])) for i in range(1, len(layer_sizes))] + + # Initialize biases for every neuron in all layers + # typed_biases = typed.List() + # for i in range(1, len(layer_sizes)): + # typed_biases.append(np.random.uniform(low, high, (layer_sizes[i], 1))) + # print(typeof(typed_biases)) + typed_biases = [np.random.uniform(low, high, (layer_sizes[i],)) for i in range(1, len(layer_sizes))] + + # Initialize empty list of output of every neuron in all layers. + # typed_layer_outputs = typed.List() + # for i in range(len(layer_sizes)): + # typed_layer_outputs.append(np.zeros((layer_sizes[i], 1))) + # print(typeof(typed_layer_outputs)) + typed_layer_outputs = [np.zeros((layer_sizes[i],1)) for i in range(len(layer_sizes))] + + typed_learning_rate = learning_rate + return NeuralNetwork(typed_layer_sizes, typed_layer_activations, typed_weights, typed_biases, typed_layer_outputs, typed_learning_rate) + + +# @njit +def calculate_output(input_data, nn): + assert input_data.shape[1] == nn.layer_sizes[0] + y = input_data + for i in prange(len(nn.weights)): + y = nn.layer_activations[i](np.dot(y, nn.weights[i]) + nn.biases[i], False) + return y + + +# @njit +def feed_forward_layers(input_data, nn): + assert input_data.shape[1] == nn.layer_sizes[0] + nn.layer_outputs[0] = input_data + for i in prange(len(nn.weights)): + ac = np.dot(nn.layer_outputs[i], nn.weights[i]) + nn.biases[i] + nn.layer_outputs[i+1] = nn.layer_activations[i](ac, False) + +def train_batch(input_data, desired_output_data, nn): + feed_forward_layers(input_data, nn) + error = (desired_output_data - nn.layer_outputs[-1]) * nn.layer_activations[-1](nn.layer_outputs[-1], True) + + temp_weights = [] + temp_biases = [] + + temp_weights.insert(0, nn.weights[-1] + nn.learning_rate * np.dot(nn.layer_outputs[-2].T, error) / input_data.shape[0]) + temp_biases.insert(0, nn.biases[-1] + nn.learning_rate * np.average(error, axis=0)) + + length_weights = len(nn.weights) + for i in range(1, length_weights): + i = length_weights - i - 1 + error = np.dot(error, nn.weights[i+1].T) * nn.layer_activations[i](nn.layer_outputs[i+1], True) + temp_weights.insert(0, nn.weights[i] + nn.learning_rate * np.dot(nn.layer_outputs[i].T, error) / input_data.shape[0]) + temp_biases.insert(0, nn.biases[i] + nn.learning_rate * np.average(error, axis=0)) + + nn.weights = temp_weights + nn.biases = temp_biases + + +# @njit(parallel=True) +def calculate_MSE(input_data, desired_output_data, nn): + assert input_data.shape[0] == desired_output_data.shape[0] + sum_error = np.sum(np.power(desired_output_data - calculate_output(input_data, nn), 2)) + return sum_error / len(input_data) + + +# @njit +def train_auto(train_input_data, train_desired_output_data, validate_input_data, validate_output_data, nn): + previous_mse = 1.0 + current_mse = 0.0 + epochs = 0 + batch_size = 8 + + + while(current_mse < previous_mse): + epochs += 1 + previous_mse = calculate_MSE(validate_input_data, validate_output_data, nn) + b,e = 0, batch_size + while(e < len(train_input_data) + 1): + train_batch(train_input_data[b:e], train_desired_output_data[b:e], nn) + b += batch_size + e += batch_size + current_mse = calculate_MSE(validate_input_data, validate_output_data, nn) + return epochs, current_mse + + +# @njit(parallel=True) +def evaluate(input_data, desired_output_data, nn): + output_max = calculate_output(input_data, nn).argmax(axis=1) + desired_output_max = desired_output_data.argmax(axis=1) + difference_output_max = output_max - desired_output_max + correct = np.count_nonzero(difference_output_max == 0) + return correct / input_data.shape[0] + + +# @njit +def print_weights_and_biases(nn): + weights = np.clip(nn.weights[0], 0.001, 0.999) + biases = np.clip(nn.biases[0], 0.001, 0.999) + print(weights) + print(biases) diff --git a/prepare_data.py b/prepare_data.py index 65414a8..6ee49bc 100644 --- a/prepare_data.py +++ b/prepare_data.py @@ -29,9 +29,13 @@ def prepare_ci_data(): data_output = h.import_from_csv("data/ci_targets.txt", int) data_output = np.array([h.class_to_array(np.amax(data_output), x) for x in data_output]) - data_input = data_input.reshape((len(data_input), -1, 1)) - data_output = data_output.reshape((len(data_output), -1, 1)) - np.save("data/ci_inputs", data_input) - np.save("data/ci_outputs", data_output) -prepare_mnist_data() \ No newline at end of file + # print(data_input.shape) + # print(data_output.shape) + + # data_input = data_input.reshape((len(data_input), -1, 1)) + # data_output = data_output.reshape((len(data_output), -1, 1)) + np.save("data/ci_2_inputs", data_input) + np.save("data/ci_2_outputs", data_output) + +prepare_ci_data() \ No newline at end of file diff --git a/z_helper.py b/z_helper.py index 024a6ed..9780f3a 100644 --- a/z_helper.py +++ b/z_helper.py @@ -1,5 +1,5 @@ import numpy as np -from numba import njit +from numba import njit, typeof def import_from_csv(path, data_type): @@ -19,7 +19,37 @@ def kfold(k, data, seed=99): return data[fold_size*2:], data[:fold_size], data[fold_size:fold_size*2] -@njit('float64[:, ::1](float64[:, ::1], boolean)') +@njit +def np_func(npfunc, axis, arr): + assert arr.ndim == 2 + assert axis in [0, 1] + if axis == 0: + result = np.empty(arr.shape[1]) + for i in range(len(result)): + result[i] = npfunc(arr[:, i]) + else: + result = np.empty(arr.shape[0]) + for i in range(len(result)): + result[i] = npfunc(arr[i, :]) + return result + + +@njit +def np_argmax(axis, arr): + return np_func(np.argmax, axis, arr) + + +@njit +def np_max(axis, arr): + return np_func(np.max, axis, arr) + + +@njit +def np_mean(axis, arr): + return np_func(np.mean, axis, arr) + + +# @njit('float64[:, ::1](float64[:, ::1], boolean)') def sigmoid(x, derivative): if derivative: return x * (1.0 - x) @@ -27,7 +57,7 @@ def sigmoid(x, derivative): return 1.0 / (1.0 + np.exp(-x)) -@njit('float64[:, ::1](float64[:, ::1], boolean)') +# @njit('float64[:, ::1](float64[:, ::1], boolean)') def relu(x, derivative): if derivative: return np.where(x <= 0.0, 0.0, 1.0) @@ -35,7 +65,7 @@ def relu(x, derivative): return np.maximum(0.0, x) -@njit('float64[:, ::1](float64[:, ::1], boolean)') +# @njit('float64[:, ::1](float64[:, ::1], boolean)') def leaky_relu(x, derivative): if derivative: return np.where(x <= 0.0, -0.01*x, 1.0) @@ -43,7 +73,15 @@ def leaky_relu(x, derivative): return np.maximum(-0.01*x, x) -@njit('float64[:, ::1](float64[:, ::1], boolean)') +# @njit('float64[:, ::1](float64[:, ::1], boolean)') def softmax(x, derivative): e_x = np.exp(x - np.max(x)) - return e_x / e_x.sum() + result = e_x / e_x.sum() + return result + + +# @njit('float64[:, ::1](float64[:, ::1], boolean)') +def softmax_2(x, derivative): + tmp = x - np_max(1, x).reshape(-1, 1) + exp_tmp = np.exp(tmp) + return exp_tmp / exp_tmp.sum(axis=1).reshape(-1, 1) From 6f21053fbe58c0723a5e7dcff651ef5bb9b4e4f4 Mon Sep 17 00:00:00 2001 From: Caspar Meijer Date: Sat, 2 May 2020 23:43:57 +0200 Subject: [PATCH 2/5] changed to numba, but does not work yet --- numpy_main_2.py | 2 +- numpy_neural_network_2.py | 103 ++++++++++++++++++-------------------- numpy_z_helper_2.py | 87 ++++++++++++++++++++++++++++++++ 3 files changed, 138 insertions(+), 54 deletions(-) create mode 100644 numpy_z_helper_2.py diff --git a/numpy_main_2.py b/numpy_main_2.py index dad85a3..938e309 100644 --- a/numpy_main_2.py +++ b/numpy_main_2.py @@ -1,6 +1,6 @@ import numpy as np import numpy_neural_network_2 as nn -import z_helper as h +import numpy_z_helper_2 as h import time np.set_printoptions(linewidth=200) diff --git a/numpy_neural_network_2.py b/numpy_neural_network_2.py index 21c6bc2..99aceb5 100644 --- a/numpy_neural_network_2.py +++ b/numpy_neural_network_2.py @@ -1,7 +1,7 @@ import numpy as np from numba.experimental import jitclass -from numba import njit, types, typed, prange -import z_helper as h +from numba import njit, types, typed, prange, typeof +import numpy_z_helper_2 as h import time from numba.core.errors import NumbaTypeSafetyWarning @@ -9,17 +9,15 @@ warnings.simplefilter('ignore', category=NumbaTypeSafetyWarning) -# spec = [ -# ("layer_sizes", types.ListType(types.int64)), -# ("layer_activations", types.ListType(types.FunctionType(types.float64[:, ::1](types.float64[:, ::1], types.boolean)))), -# ("weights", types.ListType(types.float64[:, ::1])), -# ("biases", types.ListType(types.float64[:, ::1])), -# ("layer_outputs", types.ListType(types.float64[:, ::1])), -# ("learning_rate", types.float64), -# ] -# @jitclass(spec) - - +spec = [ + ("layer_sizes", types.ListType(types.int64)), + ("layer_activations", types.ListType(types.FunctionType(types.float64[:, ::1](types.float64[:, ::1], types.boolean)))), + ("weights", types.ListType(types.float64[:, ::1])), + ("biases", types.ListType(types.float64[::1])), + ("layer_outputs", types.ListType(types.float64[:, ::1])), + ("learning_rate", types.float64), +] +@jitclass(spec) class NeuralNetwork: def __init__(self, layer_sizes, layer_activations, weights, biases, layer_outputs, learning_rate): self.layer_sizes = layer_sizes @@ -35,46 +33,46 @@ def make_neural_network(layer_sizes, layer_activations, learning_rate=0.05, low= assert size > 0 # Initialize typed layer sizes list. - # typed_layer_sizes = typed.List() - # for size in layer_sizes: - # typed_layer_sizes.append(size) + typed_layer_sizes = typed.List() + for size in layer_sizes: + typed_layer_sizes.append(size) # print(typeof(typed_layer_sizes)) - typed_layer_sizes = layer_sizes + # typed_layer_sizes = layer_sizes # Initialie typed layer activation method strings list. - # prototype = types.FunctionType(types.float64[:, ::1](types.float64[:, ::1], types.boolean)) - # typed_layer_activations = typed.List.empty_list(prototype) - # for activation in layer_activations: - # typed_layer_activations.append(activation) - # print(typedof(typed_layer_activations)) - typed_layer_activations = layer_activations + prototype = types.FunctionType(types.float64[:, ::1](types.float64[:, ::1], types.boolean)) + typed_layer_activations = typed.List.empty_list(prototype) + for activation in layer_activations: + typed_layer_activations.append(activation) + # print(typeof(typed_layer_activations)) + # typed_layer_activations = layer_activations # Initialize weights between every neuron in all adjacent layers. - # typed_weights = typed.List() - # for i in range(1, len(layer_sizes)): - # typed_weights.append(np.random.uniform(low, high, (layer_sizes[i-1], layer_sizes[i]))) + typed_weights = typed.List() + for i in range(1, len(layer_sizes)): + typed_weights.append(np.random.uniform(low, high, (layer_sizes[i-1], layer_sizes[i]))) # print(typeof(typed_weights)) - typed_weights = [np.random.uniform(low, high, (layer_sizes[i-1], layer_sizes[i])) for i in range(1, len(layer_sizes))] + # typed_weights = [np.random.uniform(low, high, (layer_sizes[i-1], layer_sizes[i])) for i in range(1, len(layer_sizes))] # Initialize biases for every neuron in all layers - # typed_biases = typed.List() - # for i in range(1, len(layer_sizes)): - # typed_biases.append(np.random.uniform(low, high, (layer_sizes[i], 1))) + typed_biases = typed.List() + for i in range(1, len(layer_sizes)): + typed_biases.append(np.random.uniform(low, high, (layer_sizes[i],))) # print(typeof(typed_biases)) - typed_biases = [np.random.uniform(low, high, (layer_sizes[i],)) for i in range(1, len(layer_sizes))] + # typed_biases = [np.random.uniform(low, high, (layer_sizes[i],)) for i in range(1, len(layer_sizes))] # Initialize empty list of output of every neuron in all layers. - # typed_layer_outputs = typed.List() - # for i in range(len(layer_sizes)): - # typed_layer_outputs.append(np.zeros((layer_sizes[i], 1))) + typed_layer_outputs = typed.List() + for i in range(len(layer_sizes)): + typed_layer_outputs.append(np.zeros((layer_sizes[i], 1))) # print(typeof(typed_layer_outputs)) - typed_layer_outputs = [np.zeros((layer_sizes[i],1)) for i in range(len(layer_sizes))] + # typed_layer_outputs = [np.zeros((layer_sizes[i],1)) for i in range(len(layer_sizes))] typed_learning_rate = learning_rate return NeuralNetwork(typed_layer_sizes, typed_layer_activations, typed_weights, typed_biases, typed_layer_outputs, typed_learning_rate) -# @njit +@njit def calculate_output(input_data, nn): assert input_data.shape[1] == nn.layer_sizes[0] y = input_data @@ -83,55 +81,54 @@ def calculate_output(input_data, nn): return y -# @njit +@njit def feed_forward_layers(input_data, nn): assert input_data.shape[1] == nn.layer_sizes[0] nn.layer_outputs[0] = input_data for i in prange(len(nn.weights)): - ac = np.dot(nn.layer_outputs[i], nn.weights[i]) + nn.biases[i] - nn.layer_outputs[i+1] = nn.layer_activations[i](ac, False) + nn.layer_outputs[i+1] = nn.layer_activations[i](np.dot(nn.layer_outputs[i], nn.weights[i]) + nn.biases[i], False) + +@njit def train_batch(input_data, desired_output_data, nn): feed_forward_layers(input_data, nn) error = (desired_output_data - nn.layer_outputs[-1]) * nn.layer_activations[-1](nn.layer_outputs[-1], True) - temp_weights = [] - temp_biases = [] + temp_weights = typed.List() + temp_biases = typed.List() temp_weights.insert(0, nn.weights[-1] + nn.learning_rate * np.dot(nn.layer_outputs[-2].T, error) / input_data.shape[0]) - temp_biases.insert(0, nn.biases[-1] + nn.learning_rate * np.average(error, axis=0)) + temp_biases.insert(0, nn.biases[-1] + nn.learning_rate * h.np_mean(0, error)) length_weights = len(nn.weights) for i in range(1, length_weights): i = length_weights - i - 1 error = np.dot(error, nn.weights[i+1].T) * nn.layer_activations[i](nn.layer_outputs[i+1], True) temp_weights.insert(0, nn.weights[i] + nn.learning_rate * np.dot(nn.layer_outputs[i].T, error) / input_data.shape[0]) - temp_biases.insert(0, nn.biases[i] + nn.learning_rate * np.average(error, axis=0)) + temp_biases.insert(0, nn.biases[i] + nn.learning_rate * h.np_mean(0, error)) nn.weights = temp_weights nn.biases = temp_biases -# @njit(parallel=True) +@njit(parallel=True) def calculate_MSE(input_data, desired_output_data, nn): assert input_data.shape[0] == desired_output_data.shape[0] sum_error = np.sum(np.power(desired_output_data - calculate_output(input_data, nn), 2)) return sum_error / len(input_data) -# @njit +@njit def train_auto(train_input_data, train_desired_output_data, validate_input_data, validate_output_data, nn): previous_mse = 1.0 current_mse = 0.0 epochs = 0 - batch_size = 8 - - + batch_size = train_input_data.shape[0] while(current_mse < previous_mse): epochs += 1 previous_mse = calculate_MSE(validate_input_data, validate_output_data, nn) - b,e = 0, batch_size - while(e < len(train_input_data) + 1): + b, e = 0, batch_size + while(e + batch_size < len(train_input_data) + 1): train_batch(train_input_data[b:e], train_desired_output_data[b:e], nn) b += batch_size e += batch_size @@ -139,10 +136,10 @@ def train_auto(train_input_data, train_desired_output_data, validate_input_data, return epochs, current_mse -# @njit(parallel=True) +@njit(parallel=True) def evaluate(input_data, desired_output_data, nn): - output_max = calculate_output(input_data, nn).argmax(axis=1) - desired_output_max = desired_output_data.argmax(axis=1) + output_max = h.np_argmax(1, calculate_output(input_data, nn)) + desired_output_max = h.np_argmax(1, desired_output_data) difference_output_max = output_max - desired_output_max correct = np.count_nonzero(difference_output_max == 0) return correct / input_data.shape[0] diff --git a/numpy_z_helper_2.py b/numpy_z_helper_2.py new file mode 100644 index 0000000..71dbd6b --- /dev/null +++ b/numpy_z_helper_2.py @@ -0,0 +1,87 @@ +import numpy as np +from numba import njit, typeof + + +def import_from_csv(path, data_type): + return np.genfromtxt(path, dtype=data_type, delimiter=',') + + +def class_to_array(maximum_class, x): + data = np.zeros(maximum_class) + 0.01 + data[x-1] = 0.99 + return data + + +def kfold(k, data, seed=99): + np.random.seed(seed) + data = np.random.permutation(data) + fold_size = int(len(data) / k) + return data[fold_size*2:], data[:fold_size], data[fold_size:fold_size*2] + + +@njit +def np_func(npfunc, axis, arr): + assert arr.ndim == 2 + assert axis in [0, 1] + if axis == 0: + result = np.empty(arr.shape[1]) + for i in range(len(result)): + result[i] = npfunc(arr[:, i]) + else: + result = np.empty(arr.shape[0]) + for i in range(len(result)): + result[i] = npfunc(arr[i, :]) + return result + + +@njit +def np_argmax(axis, arr): + return np_func(np.argmax, axis, arr) + + +@njit +def np_max(axis, arr): + return np_func(np.max, axis, arr) + + +@njit +def np_mean(axis, arr): + return np_func(np.mean, axis, arr) + + +@njit('float64[:, ::1](float64[:, ::1], boolean)') +def sigmoid(x, derivative): + if derivative: + return x * (1.0 - x) + else: + return 1.0 / (1.0 + np.exp(-x)) + + +@njit('float64[:, ::1](float64[:, ::1], boolean)') +def relu(x, derivative): + if derivative: + return np.where(x <= 0.0, 0.0, 1.0) + else: + return np.maximum(0.0, x) + + +@njit('float64[:, ::1](float64[:, ::1], boolean)') +def leaky_relu(x, derivative): + if derivative: + return np.where(x <= 0.0, -0.01*x, 1.0) + else: + return np.maximum(-0.01*x, x) + + +@njit('float64[:, ::1](float64[:, ::1], boolean)') +def softmax(x, derivative): + e_x = np.exp(x - np.max(x)) + result = e_x / e_x.sum() + return result + + +@njit('float64[:, ::1](float64[:, ::1], boolean)') +def softmax_2(x, derivative): + tmp = x - np_max(1, x).reshape(-1, 1) + exp_tmp = np.exp(tmp) + return exp_tmp / exp_tmp.sum(axis=1).reshape(-1, 1) From 40b8de112d67e0793a97f7d681507944d0343cc8 Mon Sep 17 00:00:00 2001 From: Caspar Meijer Date: Sun, 3 May 2020 12:04:54 +0200 Subject: [PATCH 3/5] numpy with numba --- numpy_main_2.py | 2 +- numpy_neural_network_2.py | 22 +++++++++++++++------- numpy_z_helper_2.py | 3 +++ z_helper.py | 15 ++++----------- 4 files changed, 23 insertions(+), 19 deletions(-) diff --git a/numpy_main_2.py b/numpy_main_2.py index 938e309..34b121f 100644 --- a/numpy_main_2.py +++ b/numpy_main_2.py @@ -20,7 +20,7 @@ np.random.seed(420) total_accuracy = 0.0 begin_total = time.time_ns() -n = 10 +n = 3 for i in range(n): random_seed = np.random.randint(10, 1010) diff --git a/numpy_neural_network_2.py b/numpy_neural_network_2.py index 99aceb5..17cf34a 100644 --- a/numpy_neural_network_2.py +++ b/numpy_neural_network_2.py @@ -37,7 +37,6 @@ def make_neural_network(layer_sizes, layer_activations, learning_rate=0.05, low= for size in layer_sizes: typed_layer_sizes.append(size) # print(typeof(typed_layer_sizes)) - # typed_layer_sizes = layer_sizes # Initialie typed layer activation method strings list. prototype = types.FunctionType(types.float64[:, ::1](types.float64[:, ::1], types.boolean)) @@ -45,27 +44,29 @@ def make_neural_network(layer_sizes, layer_activations, learning_rate=0.05, low= for activation in layer_activations: typed_layer_activations.append(activation) # print(typeof(typed_layer_activations)) - # typed_layer_activations = layer_activations # Initialize weights between every neuron in all adjacent layers. typed_weights = typed.List() for i in range(1, len(layer_sizes)): typed_weights.append(np.random.uniform(low, high, (layer_sizes[i-1], layer_sizes[i]))) # print(typeof(typed_weights)) - # typed_weights = [np.random.uniform(low, high, (layer_sizes[i-1], layer_sizes[i])) for i in range(1, len(layer_sizes))] # Initialize biases for every neuron in all layers typed_biases = typed.List() for i in range(1, len(layer_sizes)): typed_biases.append(np.random.uniform(low, high, (layer_sizes[i],))) # print(typeof(typed_biases)) - # typed_biases = [np.random.uniform(low, high, (layer_sizes[i],)) for i in range(1, len(layer_sizes))] # Initialize empty list of output of every neuron in all layers. typed_layer_outputs = typed.List() for i in range(len(layer_sizes)): typed_layer_outputs.append(np.zeros((layer_sizes[i], 1))) # print(typeof(typed_layer_outputs)) + + # typed_layer_sizes = layer_sizes + # typed_layer_activations = layer_activations + # typed_weights = [np.random.uniform(low, high, (layer_sizes[i-1], layer_sizes[i])) for i in range(1, len(layer_sizes))] + # typed_biases = [np.random.uniform(low, high, (layer_sizes[i],)) for i in range(1, len(layer_sizes))] # typed_layer_outputs = [np.zeros((layer_sizes[i],1)) for i in range(len(layer_sizes))] typed_learning_rate = learning_rate @@ -96,7 +97,11 @@ def train_batch(input_data, desired_output_data, nn): temp_weights = typed.List() temp_biases = typed.List() + # temp_weights = [] + # temp_biases = [] + # nn.weights[-1] += nn.learning_rate * np.dot(nn.layer_outputs[-2].T, error) / input_data.shape[0] + # nn.biases[-1] += nn.learning_rate * h.np_mean(0, error) temp_weights.insert(0, nn.weights[-1] + nn.learning_rate * np.dot(nn.layer_outputs[-2].T, error) / input_data.shape[0]) temp_biases.insert(0, nn.biases[-1] + nn.learning_rate * h.np_mean(0, error)) @@ -104,6 +109,8 @@ def train_batch(input_data, desired_output_data, nn): for i in range(1, length_weights): i = length_weights - i - 1 error = np.dot(error, nn.weights[i+1].T) * nn.layer_activations[i](nn.layer_outputs[i+1], True) + # nn.weights[i] += nn.learning_rate * np.dot(nn.layer_outputs[i].T, error) / input_data.shape[0] + # nn.biases[i] += nn.learning_rate * h.np_mean(0, error) temp_weights.insert(0, nn.weights[i] + nn.learning_rate * np.dot(nn.layer_outputs[i].T, error) / input_data.shape[0]) temp_biases.insert(0, nn.biases[i] + nn.learning_rate * h.np_mean(0, error)) @@ -123,12 +130,13 @@ def train_auto(train_input_data, train_desired_output_data, validate_input_data, previous_mse = 1.0 current_mse = 0.0 epochs = 0 - batch_size = train_input_data.shape[0] + batch_size = 8 while(current_mse < previous_mse): + # while(epochs < 40): epochs += 1 previous_mse = calculate_MSE(validate_input_data, validate_output_data, nn) b, e = 0, batch_size - while(e + batch_size < len(train_input_data) + 1): + while(e + batch_size <= len(train_input_data)): train_batch(train_input_data[b:e], train_desired_output_data[b:e], nn) b += batch_size e += batch_size @@ -145,7 +153,7 @@ def evaluate(input_data, desired_output_data, nn): return correct / input_data.shape[0] -# @njit +@njit def print_weights_and_biases(nn): weights = np.clip(nn.weights[0], 0.001, 0.999) biases = np.clip(nn.biases[0], 0.001, 0.999) diff --git a/numpy_z_helper_2.py b/numpy_z_helper_2.py index 71dbd6b..42c513a 100644 --- a/numpy_z_helper_2.py +++ b/numpy_z_helper_2.py @@ -36,16 +36,19 @@ def np_func(npfunc, axis, arr): @njit def np_argmax(axis, arr): + # return arr.argmax(axis=axis) return np_func(np.argmax, axis, arr) @njit def np_max(axis, arr): + # return arr.max(axis=axis) return np_func(np.max, axis, arr) @njit def np_mean(axis, arr): + # return arr.mean(axis=axis) return np_func(np.mean, axis, arr) diff --git a/z_helper.py b/z_helper.py index 9780f3a..86390ca 100644 --- a/z_helper.py +++ b/z_helper.py @@ -49,7 +49,7 @@ def np_mean(axis, arr): return np_func(np.mean, axis, arr) -# @njit('float64[:, ::1](float64[:, ::1], boolean)') +@njit('float64[:, ::1](float64[:, ::1], boolean)') def sigmoid(x, derivative): if derivative: return x * (1.0 - x) @@ -57,7 +57,7 @@ def sigmoid(x, derivative): return 1.0 / (1.0 + np.exp(-x)) -# @njit('float64[:, ::1](float64[:, ::1], boolean)') +@njit('float64[:, ::1](float64[:, ::1], boolean)') def relu(x, derivative): if derivative: return np.where(x <= 0.0, 0.0, 1.0) @@ -65,7 +65,7 @@ def relu(x, derivative): return np.maximum(0.0, x) -# @njit('float64[:, ::1](float64[:, ::1], boolean)') +@njit('float64[:, ::1](float64[:, ::1], boolean)') def leaky_relu(x, derivative): if derivative: return np.where(x <= 0.0, -0.01*x, 1.0) @@ -73,15 +73,8 @@ def leaky_relu(x, derivative): return np.maximum(-0.01*x, x) -# @njit('float64[:, ::1](float64[:, ::1], boolean)') +@njit('float64[:, ::1](float64[:, ::1], boolean)') def softmax(x, derivative): e_x = np.exp(x - np.max(x)) result = e_x / e_x.sum() return result - - -# @njit('float64[:, ::1](float64[:, ::1], boolean)') -def softmax_2(x, derivative): - tmp = x - np_max(1, x).reshape(-1, 1) - exp_tmp = np.exp(tmp) - return exp_tmp / exp_tmp.sum(axis=1).reshape(-1, 1) From 8c1771d2cf63110e9dc46a9bfdde293f6d8311e7 Mon Sep 17 00:00:00 2001 From: Caspar Meijer Date: Mon, 4 May 2020 01:29:49 +0200 Subject: [PATCH 4/5] prepare for parallel of back propagation --- numpy_main_2.py | 29 ++++++++------- numpy_neural_network_2.py | 75 ++++++++++++++++++++------------------- numpy_z_helper_2.py | 3 +- prepare_data.py | 14 ++++---- 4 files changed, 63 insertions(+), 58 deletions(-) diff --git a/numpy_main_2.py b/numpy_main_2.py index 34b121f..9b629b6 100644 --- a/numpy_main_2.py +++ b/numpy_main_2.py @@ -4,23 +4,25 @@ import time np.set_printoptions(linewidth=200) -data_input = np.load("data/ci_2_inputs.npy") -data_output = np.load("data/ci_2_outputs.npy") +data_input = np.load("data/mnist_2_inputs.npy") +data_output = np.load("data/mnist_2_outputs.npy") print(data_input.shape) print(data_output.shape) print("Begin compiling!") +np.random.seed(420) begin_time = time.time_ns() compiled_nn_values = nn.make_neural_network(layer_sizes=[data_input.shape[1], data_output.shape[1]], layer_activations=[h.sigmoid]) -nn.train_auto(data_input[:10], data_output[:10], data_input[10: 15], data_output[10: 15], compiled_nn_values) -end_time = time.time_ns() -print("Compile time:", (end_time-begin_time) / 1e9) +print("Created Neural network lists:", (time.time_ns()-begin_time) / 1e9) +nn.train_auto(data_input[:10], data_output[:10], data_input[10: 15], data_output[10: 15], 5, 500, compiled_nn_values) +print("Compile time:", (time.time_ns()-begin_time) / 1e9) -np.random.seed(420) total_accuracy = 0.0 -begin_total = time.time_ns() -n = 3 +total_time = 0.0 +n = 5 +batch_size = 16 +max_epochs = 200 for i in range(n): random_seed = np.random.randint(10, 1010) @@ -28,16 +30,17 @@ train_input, validate_input, test_input = h.kfold(7, data_input, random_seed) train_output, validate_output, test_output = h.kfold(7, data_output, random_seed) - nn_values = nn.make_neural_network(layer_sizes=[train_input.shape[1], 20, train_output.shape[1]], layer_activations=[h.sigmoid, h.softmax_2]) + nn_values = nn.make_neural_network(layer_sizes=[train_input.shape[1], train_output.shape[1]], layer_activations=[h.softmax_2]) begin_time = time.time_ns() - epochs, current_mse = nn.train_auto(train_input, train_output, validate_input, validate_output, nn_values) + epochs, current_mse = nn.train_auto(train_input, train_output, validate_input, validate_output, batch_size, max_epochs, nn_values) end_time = time.time_ns() train_mse = nn.calculate_MSE(train_input, train_output, nn_values) test_mse = nn.calculate_MSE(test_input, test_output, nn_values) - accuracy_test = nn.evaluate(test_input, test_output, nn_values) + total_accuracy += accuracy_test - print("Seed:", random_seed, "Epochs:", epochs, "Time:", (end_time-begin_time)/1e9, "Accuracy:", accuracy_test, "Tr:", train_mse, "V:", current_mse, "T:", test_mse) -print("Average Accuracy:", total_accuracy / n, "Average Time:", ((time.time_ns()-begin_total)/1e9) / n) + total_time += (end_time-begin_time)/1e9 + print("Seed:", random_seed, "Batch_size:", batch_size, "Epochs:", epochs, "Time:", (end_time-begin_time)/1e9, "Accuracy:", accuracy_test, "Tr:", train_mse, "V:", current_mse, "T:", test_mse) +print("Average Accuracy:", total_accuracy / n, "Average Time:", total_time / n) diff --git a/numpy_neural_network_2.py b/numpy_neural_network_2.py index 17cf34a..c00bc0b 100644 --- a/numpy_neural_network_2.py +++ b/numpy_neural_network_2.py @@ -9,11 +9,13 @@ warnings.simplefilter('ignore', category=NumbaTypeSafetyWarning) +weights_type = types.float64[:, ::1] +biases_type = types.float64[::1] spec = [ ("layer_sizes", types.ListType(types.int64)), ("layer_activations", types.ListType(types.FunctionType(types.float64[:, ::1](types.float64[:, ::1], types.boolean)))), - ("weights", types.ListType(types.float64[:, ::1])), - ("biases", types.ListType(types.float64[::1])), + ("weights", types.ListType(weights_type)), + ("biases", types.ListType(biases_type)), ("layer_outputs", types.ListType(types.float64[:, ::1])), ("learning_rate", types.float64), ] @@ -36,43 +38,46 @@ def make_neural_network(layer_sizes, layer_activations, learning_rate=0.05, low= typed_layer_sizes = typed.List() for size in layer_sizes: typed_layer_sizes.append(size) - # print(typeof(typed_layer_sizes)) # Initialie typed layer activation method strings list. prototype = types.FunctionType(types.float64[:, ::1](types.float64[:, ::1], types.boolean)) typed_layer_activations = typed.List.empty_list(prototype) for activation in layer_activations: typed_layer_activations.append(activation) - # print(typeof(typed_layer_activations)) # Initialize weights between every neuron in all adjacent layers. typed_weights = typed.List() for i in range(1, len(layer_sizes)): typed_weights.append(np.random.uniform(low, high, (layer_sizes[i-1], layer_sizes[i]))) - # print(typeof(typed_weights)) # Initialize biases for every neuron in all layers typed_biases = typed.List() for i in range(1, len(layer_sizes)): typed_biases.append(np.random.uniform(low, high, (layer_sizes[i],))) - # print(typeof(typed_biases)) # Initialize empty list of output of every neuron in all layers. typed_layer_outputs = typed.List() for i in range(len(layer_sizes)): typed_layer_outputs.append(np.zeros((layer_sizes[i], 1))) - # print(typeof(typed_layer_outputs)) - - # typed_layer_sizes = layer_sizes - # typed_layer_activations = layer_activations - # typed_weights = [np.random.uniform(low, high, (layer_sizes[i-1], layer_sizes[i])) for i in range(1, len(layer_sizes))] - # typed_biases = [np.random.uniform(low, high, (layer_sizes[i],)) for i in range(1, len(layer_sizes))] - # typed_layer_outputs = [np.zeros((layer_sizes[i],1)) for i in range(len(layer_sizes))] typed_learning_rate = learning_rate return NeuralNetwork(typed_layer_sizes, typed_layer_activations, typed_weights, typed_biases, typed_layer_outputs, typed_learning_rate) +@njit +def update_parameters(weights, biases, nn): + # temp_weights = typed.List() + # temp_biases = typed.List() + # for i in range(len(weights)): + # temp_weights.append(weights[i]) + # temp_biases.append(biases[i]) + # nn.weights = temp_weights + # nn.biases = temp_biases + + nn.weights = weights + nn.biases = biases + + @njit def calculate_output(input_data, nn): assert input_data.shape[1] == nn.layer_sizes[0] @@ -91,53 +96,51 @@ def feed_forward_layers(input_data, nn): @njit -def train_batch(input_data, desired_output_data, nn): - feed_forward_layers(input_data, nn) - error = (desired_output_data - nn.layer_outputs[-1]) * nn.layer_activations[-1](nn.layer_outputs[-1], True) +def propogate_back(input_data, desired_output_data, nn): - temp_weights = typed.List() - temp_biases = typed.List() - # temp_weights = [] - # temp_biases = [] + length_weights = len(nn.weights) + temp_weights = typed.List.empty_list(item_type=weights_type) + temp_biases = typed.List.empty_list(item_type=biases_type) + # temp_weights = typed.Dict.empty(key_type=types.int64, value_type=weights_type) + # temp_biases = typed.Dict.empty(key_type=types.int64, value_type=biases_type) - # nn.weights[-1] += nn.learning_rate * np.dot(nn.layer_outputs[-2].T, error) / input_data.shape[0] - # nn.biases[-1] += nn.learning_rate * h.np_mean(0, error) + error = (desired_output_data - nn.layer_outputs[-1]) * nn.layer_activations[-1](nn.layer_outputs[-1], True) temp_weights.insert(0, nn.weights[-1] + nn.learning_rate * np.dot(nn.layer_outputs[-2].T, error) / input_data.shape[0]) temp_biases.insert(0, nn.biases[-1] + nn.learning_rate * h.np_mean(0, error)) + # temp_weights[length_weights - 1] = nn.weights[-1] + nn.learning_rate * np.dot(nn.layer_outputs[-2].T, error) / input_data.shape[0] + # temp_biases[length_weights - 1] = nn.biases[-1] + nn.learning_rate * h.np_mean(0, error) - length_weights = len(nn.weights) - for i in range(1, length_weights): - i = length_weights - i - 1 + for p in range(1, length_weights): + i = length_weights - p - 1 error = np.dot(error, nn.weights[i+1].T) * nn.layer_activations[i](nn.layer_outputs[i+1], True) - # nn.weights[i] += nn.learning_rate * np.dot(nn.layer_outputs[i].T, error) / input_data.shape[0] - # nn.biases[i] += nn.learning_rate * h.np_mean(0, error) temp_weights.insert(0, nn.weights[i] + nn.learning_rate * np.dot(nn.layer_outputs[i].T, error) / input_data.shape[0]) temp_biases.insert(0, nn.biases[i] + nn.learning_rate * h.np_mean(0, error)) + # temp_weights[i] = nn.weights[i] + nn.learning_rate * np.dot(nn.layer_outputs[i].T, error) / input_data.shape[0] + # temp_biases[i] = nn.biases[i] + nn.learning_rate * h.np_mean(0, error) - nn.weights = temp_weights - nn.biases = temp_biases + return temp_weights, temp_biases @njit(parallel=True) def calculate_MSE(input_data, desired_output_data, nn): assert input_data.shape[0] == desired_output_data.shape[0] sum_error = np.sum(np.power(desired_output_data - calculate_output(input_data, nn), 2)) - return sum_error / len(input_data) + return sum_error / input_data.shape[0] @njit -def train_auto(train_input_data, train_desired_output_data, validate_input_data, validate_output_data, nn): +def train_auto(train_input_data, train_desired_output_data, validate_input_data, validate_output_data, batch_size, max_epochs, nn): previous_mse = 1.0 current_mse = 0.0 epochs = 0 - batch_size = 8 - while(current_mse < previous_mse): - # while(epochs < 40): + while(current_mse < previous_mse and epochs < max_epochs): epochs += 1 previous_mse = calculate_MSE(validate_input_data, validate_output_data, nn) b, e = 0, batch_size - while(e + batch_size <= len(train_input_data)): - train_batch(train_input_data[b:e], train_desired_output_data[b:e], nn) + while(e <= len(train_input_data)): + feed_forward_layers(train_input_data[b:e], nn) + temp_weights, temp_biases = propogate_back(train_input_data[b:e], train_desired_output_data[b:e], nn) + update_parameters(temp_weights, temp_biases, nn) b += batch_size e += batch_size current_mse = calculate_MSE(validate_input_data, validate_output_data, nn) diff --git a/numpy_z_helper_2.py b/numpy_z_helper_2.py index 42c513a..6fe2757 100644 --- a/numpy_z_helper_2.py +++ b/numpy_z_helper_2.py @@ -1,5 +1,5 @@ import numpy as np -from numba import njit, typeof +from numba import njit def import_from_csv(path, data_type): @@ -51,7 +51,6 @@ def np_mean(axis, arr): # return arr.mean(axis=axis) return np_func(np.mean, axis, arr) - @njit('float64[:, ::1](float64[:, ::1], boolean)') def sigmoid(x, derivative): if derivative: diff --git a/prepare_data.py b/prepare_data.py index 6ee49bc..7ccd171 100644 --- a/prepare_data.py +++ b/prepare_data.py @@ -15,13 +15,13 @@ def prepare_mnist_data(): data_output = np.array([h.class_to_array(np.amax(data_output), x) for x in data_output]) print(6) - data_input = data_input.reshape((len(data_input), -1, 1)) - print(7) - data_output = data_output.reshape((len(data_output), -1, 1)) - print(8) + # data_input = data_input.reshape((len(data_input), -1, 1)) + # print(7) + # data_output = data_output.reshape((len(data_output), -1, 1)) + # print(8) - np.save("data/mnist_inputs", data_input) - np.save("data/mnist_outputs", data_output) + np.save("data/mnist_2_inputs", data_input) + np.save("data/mnist_2_outputs", data_output) def prepare_ci_data(): @@ -38,4 +38,4 @@ def prepare_ci_data(): np.save("data/ci_2_inputs", data_input) np.save("data/ci_2_outputs", data_output) -prepare_ci_data() \ No newline at end of file +prepare_mnist_data() \ No newline at end of file From 254a4f15a59cf8bd25b5a6e9575831b5d5b55fef Mon Sep 17 00:00:00 2001 From: Caspar Meijer Date: Mon, 4 May 2020 01:30:58 +0200 Subject: [PATCH 5/5] Include max epochs for single training main --- .gitignore | 4 ++++ main.py | 23 +++++++++++++---------- numba_neural_network.py | 12 ++++++------ z_helper.py | 30 ------------------------------ 4 files changed, 23 insertions(+), 46 deletions(-) diff --git a/.gitignore b/.gitignore index 956a40e..d6316d1 100644 --- a/.gitignore +++ b/.gitignore @@ -5,3 +5,7 @@ data/mnist_train.csv data/mnist_outputs.npy data/mnist_inputs.npy other/mlp.ipynb + +data/mnist_2_outputs.npy + +data/mnist_2_inputs.npy diff --git a/main.py b/main.py index 5ceff63..653a207 100644 --- a/main.py +++ b/main.py @@ -4,23 +4,25 @@ import time np.set_printoptions(linewidth=200) -data_input = np.load("data/ci_inputs.npy") -data_output = np.load("data/ci_outputs.npy") +data_input = np.load("data/mnist_inputs.npy") +data_output = np.load("data/mnist_outputs.npy") print(data_input.shape) print(data_output.shape) print("Begin compiling!") +np.random.seed(420) begin_time = time.time_ns() compiled_nn_values = nn.make_neural_network(layer_sizes=[data_input.shape[1], data_output.shape[1]], layer_activations=[h.softmax]) -nn.train_auto(data_input[:100], data_output[:100], data_input[100: 140], data_output[100: 140], compiled_nn_values) +print("Created Neural network lists:", (time.time_ns()-begin_time) / 1e9) +nn.train_auto(data_input[:100], data_output[:100], data_input[100: 140], data_output[100: 140], 10, compiled_nn_values) end_time = time.time_ns() print("Compile time:", (end_time-begin_time) / 1e9) -np.random.seed(420) total_accuracy = 0.0 -begin_total = time.time_ns() -n = 10 +total_time = 0.0 +n = 5 +max_epochs = 200 for i in range(n): random_seed = np.random.randint(10, 1010) @@ -29,16 +31,17 @@ train_input, validate_input, test_input = h.kfold(7, data_input, random_seed) train_output, validate_output, test_output = h.kfold(7, data_output, random_seed) - nn_values = nn.make_neural_network(layer_sizes=[train_input.shape[1], 20, train_output.shape[1]], layer_activations=[h.sigmoid, h.softmax]) + nn_values = nn.make_neural_network(layer_sizes=[train_input.shape[1], train_output.shape[1]], layer_activations=[h.softmax]) begin_time = time.time_ns() - epochs, current_mse = nn.train_auto(train_input, train_output, validate_input, validate_output, nn_values) + epochs, current_mse = nn.train_auto(train_input, train_output, validate_input, validate_output, max_epochs, nn_values) end_time = time.time_ns() train_mse = nn.calculate_MSE(train_input, train_output, nn_values) test_mse = nn.calculate_MSE(test_input, test_output, nn_values) - accuracy_test = nn.evaluate(test_input, test_output, nn_values) + total_accuracy += accuracy_test + total_time += (end_time-begin_time)/1e9 print("Seed:", random_seed, "Epochs:", epochs, "Time:", (end_time-begin_time)/1e9, "Accuracy:", accuracy_test, "Tr:", train_mse, "V:", current_mse, "T:", test_mse) -print("Average Accuracy:", total_accuracy / n, "Average Time:", ((time.time_ns()-begin_total)/1e9) / n) +print("Average Accuracy:", total_accuracy / n, "Average Time:", total_time / n) diff --git a/numba_neural_network.py b/numba_neural_network.py index 0e7861d..6b9bdb0 100644 --- a/numba_neural_network.py +++ b/numba_neural_network.py @@ -70,7 +70,7 @@ def make_neural_network(layer_sizes, layer_activations, learning_rate=0.05, low= def calculate_output(input_data, nn): assert len(input_data) == nn.layer_sizes[0] y = input_data - for i in prange(len(nn.weights)): + for i in range(len(nn.weights)): y = nn.layer_activations[i](np.dot(nn.weights[i].T, y) + nn.biases[i], False) return y @@ -79,7 +79,7 @@ def calculate_output(input_data, nn): def feed_forward_layers(input_data, nn): assert len(input_data) == nn.layer_sizes[0] nn.layer_outputs[0] = input_data - for i in prange(len(nn.weights)): + for i in range(len(nn.weights)): nn.layer_outputs[i+1] = nn.layer_activations[i](np.dot(nn.weights[i].T, nn.layer_outputs[i]) + nn.biases[i], False) @@ -94,8 +94,8 @@ def train_single(input_data, desired_output_data, nn): nn.biases[-1] += nn.learning_rate * error length_weights = len(nn.weights) - for i in prange(1, length_weights): - i = length_weights - i - 1 + for p in range(1, length_weights): + i = length_weights - p - 1 error = np.dot(nn.weights[i+1], error) * nn.layer_activations[i](nn.layer_outputs[i+1], True) nn.weights[i] += nn.learning_rate * nn.layer_outputs[i] * error.T nn.biases[i] += nn.learning_rate * error @@ -124,11 +124,11 @@ def train_epoch(train_input_data, train_desired_output_data, validate_input_data @njit -def train_auto(train_input_data, train_desired_output_data, validate_input_data, validate_output_data, nn): +def train_auto(train_input_data, train_desired_output_data, validate_input_data, validate_output_data, max_epochs, nn): previous_mse = 1.0 current_mse = 0.0 epochs = 0 - while(current_mse < previous_mse): + while(current_mse < previous_mse and epochs < max_epochs): epochs += 1 previous_mse = calculate_MSE(validate_input_data, validate_output_data, nn) for i in range(len(train_input_data)): diff --git a/z_helper.py b/z_helper.py index 86390ca..93b5511 100644 --- a/z_helper.py +++ b/z_helper.py @@ -19,36 +19,6 @@ def kfold(k, data, seed=99): return data[fold_size*2:], data[:fold_size], data[fold_size:fold_size*2] -@njit -def np_func(npfunc, axis, arr): - assert arr.ndim == 2 - assert axis in [0, 1] - if axis == 0: - result = np.empty(arr.shape[1]) - for i in range(len(result)): - result[i] = npfunc(arr[:, i]) - else: - result = np.empty(arr.shape[0]) - for i in range(len(result)): - result[i] = npfunc(arr[i, :]) - return result - - -@njit -def np_argmax(axis, arr): - return np_func(np.argmax, axis, arr) - - -@njit -def np_max(axis, arr): - return np_func(np.max, axis, arr) - - -@njit -def np_mean(axis, arr): - return np_func(np.mean, axis, arr) - - @njit('float64[:, ::1](float64[:, ::1], boolean)') def sigmoid(x, derivative): if derivative: pFad - Phonifier reborn

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