#!/usr/bin/env python3
import sys
import numpy as np
from keras.layers import Dense, Reshape
from keras.layers import BatchNormalization
from keras.layers.advanced_activations import LeakyReLU
from keras.models import Sequential, Model
from keras.optimizers import Adam
from keras.utils import plot_model
class Generator(object):
def __init__(self, width = 28, height= 28, channels = 1, latent_size=100):
self.W = width
self.H = height
self.C = channels
self.OPTIMIZER = Adam(lr=0.0002, decay=8e-9)
self.LATENT_SPACE_SIZE = latent_size
self.latent_space = np.random.normal(0,1,(self.LATENT_SPACE_SIZE,))
self.Generator = self.model()
self.Generator.compile(loss='binary_crossentropy', optimizer=self.OPTIMIZER)
self.save_model()
self.summary()
def model(self, block_starting_size=128,num_blocks=4):
model = Sequential()
block_size = block_starting_size
model.add(Dense(block_size, input_shape=(self.LATENT_SPACE_SIZE,)))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
for i in range(num_blocks-1):
block_size = block_size * 2
model.add(Dense(block_size))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(self.W * self.H * self.C, activation='tanh'))
model.add(Reshape((self.W, self.H, self.C)))
return model
def summary(self):
return self.Generator.summary()
def save_model(self):
plot_model(self.Generator.model, to_file='/data/Generator_Model.png')
