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data_mining_gan/gan/gan.py
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# Import | |
import os | |
# Data Mangement | |
import numpy as np | |
import pandas as pd | |
from sklearn.cross_validation import train_test_split | |
from sklearn.utils import resample | |
# import matplotlib.pyplot as plt | |
# Nuerual Net Building | |
from keras import layers, initializers, regularizers | |
from keras.layers import Input, Dense, ActivityRegularization, InputLayer, Reshape, BatchNormalization, Flatten | |
from keras.models import Sequential, Model | |
from keras.optimizers import Adam | |
from keras.utils.generic_utils import Progbar | |
# Third party keras gan tool | |
from keras_adversarial import AdversarialModel, simple_gan, gan_targets | |
from keras_adversarial import AdversarialOptimizerSimultaneous, normal_latent_sampling | |
################################################# | |
# Constants | |
################################################# | |
################################################# | |
# Functions | |
################################################# | |
def permute_sample(dataset, new_dataset_num_rows, as_data_frame=False): | |
""" | |
Given a 2-D pandas dataframe it will create a permutaiton | |
of each column. | |
""" | |
new_dataset = resample(dataset.iloc[:, 0], | |
n_samples=new_dataset_num_rows).reshape(new_dataset_num_rows, 1) | |
for col_index in range(1, dataset.shape[1]): | |
new_col = resample(dataset.iloc[:, col_index], | |
n_samples=new_dataset_num_rows).reshape(new_dataset_num_rows, 1) | |
new_dataset = np.append(new_dataset, new_col, 1) | |
return pd.DataFrame(new_dataset) if as_data_frame else new_dataset | |
def make_3d_dataset(dataset, new_dataset_num_rows, depth): | |
""" | |
Takes a 2 dimensional dataframe and makes it into a | |
3 dimensional dataframe by creating more 2D dataframes | |
and putting them together. New dataframes are made by | |
resampling (with replacement) the columns of the original dataset. | |
""" | |
height, width = dataset.shape | |
new_dataset = permute_sample(dataset, new_dataset_num_rows).reshape(new_dataset_num_rows, width, 1) | |
for _ in range(depth-1): | |
new_layer = permute_sample(dataset, new_dataset_num_rows).reshape(new_dataset_num_rows, width, 1) | |
new_dataset = np.append(new_dataset, new_layer, 2) | |
return new_dataset | |
################################################# | |
# Load data | |
################################################# | |
base_path = "../mkdataset/datasets/gan_datasets/" | |
# label_file_name = "tham_human_and_mouse_dataset.csv" | |
file_name = "tham_lasso_dataset.csv" | |
all_data = pd.read_csv(os.path.join(base_path, file_name)) | |
#--------------------------------------- | |
# Prepare Data | |
#--------------------------------------- | |
just_values = all_data.iloc[1:, 3:] | |
x_train = make_3d_dataset(just_values, 300, 100) | |
################################################# | |
# Variables | |
################################################# | |
# Output | |
output_base_path = './' | |
#--------------------------------------- | |
# Network Variables | |
#--------------------------------------- | |
# Layer Input Output Shapes | |
gen_input_shape = (x_train.shape[1], x_train.shape[2]) | |
discr_input_shape = (x_train.shape[1], x_train.shape[2]) | |
gen_output_shape = discr_input_shape | |
# Generator Variables | |
weight_initializer = initializers.RandomNormal(mean=0.0, stddev=0.05, seed=None) | |
bias_initializer = initializers.RandomUniform(minval=-0.05, maxval=0.05, seed=None) | |
generator_regularizer = regularizers.l1(0.000000005) | |
descriminator_regularizer = regularizers.l2(0.0000000005) | |
# Training Varaibles | |
epochs = 2 | |
batch_size = 1 # x_train.shape[0] | |
input_size = x_train.shape[1] * x_train.shape[2] * batch_size | |
# Build Generative model | |
generative_model = Sequential() | |
# generative_model.add(InputLayer(input_shape=gen_input_shape)) | |
generative_model.add(InputLayer(input_shape=discr_input_shape)) | |
generative_model.add(Flatten()) | |
generative_model.add(Dense(units=int(1.5*input_size), | |
use_bias=True, | |
activation='relu', | |
kernel_initializer=weight_initializer, | |
bias_initializer=bias_initializer, | |
kernel_regularizer=generator_regularizer)) | |
# generative_model.add(ActivityRegularization(l1=0.02)) | |
generative_model.add(Dense(units=int(input_size), | |
activation='relu', | |
kernel_initializer=weight_initializer, | |
kernel_regularizer=generator_regularizer)) | |
generative_model.add(Reshape(discr_input_shape)) | |
# Build Discriminator model | |
discriminator_model = Sequential() | |
discriminator_model.add(InputLayer(input_shape=discr_input_shape)) | |
discriminator_model.add(BatchNormalization(axis=1)) | |
discriminator_model.add(Flatten()) | |
discriminator_model.add(Dense(units=int(1.5*input_size), | |
use_bias=True, | |
activation='relu', | |
kernel_initializer=weight_initializer, | |
bias_initializer=bias_initializer, | |
kernel_regularizer=descriminator_regularizer)) | |
# discriminator_model.add(ActivityRegularization(l2=0.02)) | |
discriminator_model.add(Dense(units=int(0.2*input_size), | |
activation='relu', | |
kernel_initializer=weight_initializer, | |
bias_initializer=bias_initializer, | |
kernel_regularizer=descriminator_regularizer)) | |
discriminator_model.add(Dense(units=1, activation='sigmoid')) | |
# Build GAN | |
gan = simple_gan(generative_model, discriminator_model, normal_latent_sampling(gen_input_shape)) | |
model = AdversarialModel(base_model=gan, | |
player_params=[generative_model.trainable_weights, | |
discriminator_model.trainable_weights], | |
player_names=['generator', 'discriminator']) | |
# Other optimizer to try AdversarialOptimizerAlternating | |
model.adversarial_compile(adversarial_optimizer=AdversarialOptimizerSimultaneous(), | |
player_optimizers=['adam', 'adam'], loss='binary_crossentropy') | |
# Print Summary of Models | |
generative_model.summary() | |
discriminator_model.summary() | |
gan.summary() | |
# Train | |
# gan_targets takes as inputs the # of samples | |
training_record = model.fit(x=x_train, | |
y=gan_targets(x_train.shape[0]), | |
epochs=epochs, | |
batch_size=batch_size) | |
# Diplay plot of loss over training | |
# plt.plot(history.history['player_0_loss']) | |
# plt.plot(history.history['player_1_loss']) | |
# plt.plot(history.history['loss']) | |
# Predict (i.e. produce new samples) | |
zsamples = np.random.normal(size=(1, x_train.shape[1], x_train.shape[2])) | |
pred = generative_model.predict(zsamples) | |
print(pred) | |
# Save new samples to file | |
new_samples = pd.DataFrame(pred[0,:,:]) | |
new_samples.to_csv(os.path.join(output_base_path, 'new_samples.csv')) | |
# save training_record | |
df = pd.DataFrame(training_record.history) | |
df.to_csv(os.path.join(output_base_path, 'training_record.csv')) | |
# save models | |
generative_model.save(os.path.join(output_base_path, 'generator.h5')) | |
discriminator_model.save(os.path.join(output_base_path, "discriminator.h5")) |