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Updating GAN Code...
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d17d293df9
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@ -388,16 +388,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 43,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array(0.662747, dtype=float32)"
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"array(0.675341, dtype=float32)"
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]
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},
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"execution_count": 40,
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"execution_count": 43,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -418,7 +418,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 44,
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"metadata": {},
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"outputs": [
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{
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@ -427,7 +427,7 @@
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"60000"
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]
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},
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"execution_count": 41,
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"execution_count": 44,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -438,7 +438,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"execution_count": 45,
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"metadata": {},
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"outputs": [
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{
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@ -452,7 +452,7 @@
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"source": [
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"# Train the GAN for only 1000 images\n",
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"batch_size = 16\n",
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"display_step = 50\n",
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"display_step = 20\n",
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"cur_step = 0\n",
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"mean_generator_loss = 0\n",
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"mean_discriminator_loss = 0\n",
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@ -463,7 +463,7 @@
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"\n",
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"for epoch in tqdm(range(200)):\n",
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"\n",
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" for real in batch_iterate(batch_size, train_images[:1000]):\n",
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" for real in batch_iterate(batch_size, train_images[:500]):\n",
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" \n",
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" D_loss,D_grads = D_loss_grad(gen, disc, real, batch_size, z_dim)\n",
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"\n",
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