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EPOCHS = 50 | |
BATCH_SIZE = 128 | |
BATCH_BLOCKS = x_train.shape[0] // BATCH_SIZE | |
discriminator_losses = [] | |
gan_losses = [] | |
for epoch in range(EPOCHS): | |
for batch_index in range(BATCH_BLOCKS): | |
batch = x_train[batch_index * BATCH_SIZE:(batch_index + 1) * BATCH_SIZE].reshape(BATCH_SIZE, 784) |
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LATENT_SIZE = 16 | |
adam = keras.optimizers.Adam(lr=0.0002, beta_1=0.5) | |
with tf.device("/gpu:0"): | |
input_x_img = keras.layers.Input((784,)) | |
x = keras.layers.Dense(1024, activation="relu")(input_x_img) | |
x = keras.layers.Dense(256, activation="relu")(x) | |
x = keras.layers.Dense(1, activation="sigmoid")(x) | |
discriminator = keras.models.Model(inputs=input_x_img, outputs=x) |
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(x_train, y_train), (x_test, y_test) = mnist.load_data() | |
x_train = x_train / 256 | |
y_train = y_train / 256 | |
np.random.shuffle(x_train) |
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import tensorflow as tf | |
from tensorflow import keras | |
from tensorflow.keras.datasets import mnist | |
import numpy as np | |
from matplotlib import pyplot as plt |