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@greentec
Created April 27, 2018 12:55
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PixelCNN - MNIST
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"most of codes from https://github.com/rickiepark/pixel-rnn-tensorflow/blob/pixel-cnn/pixel-cnn.py\n",
"I just added more layers, reduced hidden layer size 64 -> 32, and added residual connection. (tf.add)\n",
"'''\n",
"\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"from scipy.misc.pilutil import toimage\n",
"\n",
"def conv2d(input, num_output, kernel_shape, mask_type, scope='conv2d'):\n",
" with tf.variable_scope(scope):\n",
" kernel_h, kernel_w = kernel_shape\n",
" assert kernel_h % 2 == 1 and kernel_w % 2 == 1, 'kernel height and width should be odd number'\n",
" center_h = kernel_h // 2\n",
" center_w = kernel_w // 2\n",
" \n",
" channel_len = input.get_shape()[-1]\n",
" mask = np.ones((kernel_h, kernel_w, channel_len, num_output), dtype=np.float32)\n",
" mask[center_h, center_w+1:, :, :] = 0.\n",
" mask[center_h+1:, :, :, :] = 0.\n",
" if mask_type == 'A':\n",
" mask[center_h, center_w, :, :] = 0.\n",
" \n",
" weight = tf.get_variable('weight', \n",
" [kernel_h, kernel_w, channel_len, num_output], \n",
" tf.float32, \n",
" tf.contrib.layers.xavier_initializer())\n",
" weight *= tf.constant(mask, dtype=tf.float32)\n",
" \n",
" value = tf.nn.conv2d(input, weight, [1, 1, 1, 1], padding='SAME', name='value')\n",
" bias = tf.get_variable('bias', [num_output], tf.float32, tf.zeros_initializer)\n",
" output = tf.nn.bias_add(value, bias, name='output')\n",
" \n",
" print('[conv2d_%s] %s : %s %s -> %s %s' % (mask_type, scope, input.name, input.get_shape(), output.name, output.get_shape()))\n",
" \n",
" return output\n",
" \n",
"def binarize(images):\n",
" return (np.random.uniform(size=images.shape) < images).astype('float32')\n",
"\n",
"\n",
"mnist = input_data.read_data_sets('data')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"height = 28\n",
"width = 28\n",
"channel = 1\n",
"batch_size = 100\n",
"hidden_dims = 32\n",
"recurrent_length = 6\n",
"out_hidden_dims = 32\n",
"out_recurrent_length = 6\n",
"learning_rate = 1e-3\n",
"\n",
"train_step = int(mnist.train.num_examples / batch_size)\n",
"test_step = int(mnist.test.num_examples / batch_size)\n",
"\n",
"print(mnist.train.num_examples, mnist.test.num_examples)\n",
"\n",
"X = tf.placeholder(tf.float32, [None, height, width, channel])\n",
"\n",
"hidden_layer = [conv2d(X, hidden_dims, [7, 7], 'A', scope='conv_A')]\n",
"for i in range(recurrent_length):\n",
" hidden_layer.append(conv2d(hidden_layer[-1], hidden_dims, [3, 3], 'B', scope='conv_B_%d' % i))\n",
" hidden_layer.append(conv2d(hidden_layer[-1], hidden_dims, [1, 1], 'B', scope='conv_B_end_%d' % i))\n",
" # residual connection\n",
" hidden_layer.append(tf.add(hidden_layer[-1], hidden_layer[-3], name='conv_residual_%d' % i))\n",
" \n",
"for i in range(out_recurrent_length):\n",
" hidden_layer.append(tf.nn.relu(conv2d(hidden_layer[-1], out_hidden_dims, [3, 3], 'B', scope='relu_B_%d' % i)))\n",
" hidden_layer.append(tf.nn.relu(conv2d(hidden_layer[-1], out_hidden_dims, [1, 1], 'B', scope='relu_B_end_%d' % i)))\n",
" # residual connection\n",
" hidden_layer.append(tf.add(hidden_layer[-1], hidden_layer[-3], name='relu_residual_%d' % i))\n",
" \n",
"y_logits = conv2d(hidden_layer[-1], 1, [1, 1], 'B', scope='y_logits')\n",
"y_ = tf.nn.sigmoid(y_logits)\n",
"\n",
"loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_logits, labels=X, name='loss'))\n",
"optimizer = tf.train.AdamOptimizer(learning_rate)\n",
"grads_and_vars = optimizer.compute_gradients(loss)\n",
"\n",
"new_grads_and_vars = [(tf.clip_by_value(gv[0], -1, 1), gv[1]) for gv in grads_and_vars]\n",
"optim = optimizer.apply_gradients(new_grads_and_vars)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"%%time\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"train_costs = []\n",
"test_costs = []\n",
"total_epoch = 100\n",
"\n",
"with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" \n",
" for epoch in range(total_epoch):\n",
" # train\n",
" total_train_costs = []\n",
" for i in range(train_step):\n",
" batch = mnist.train.next_batch(batch_size)\n",
" images = binarize(batch[0]).reshape([batch_size, height, width, channel])\n",
" _, cost = sess.run([optim, loss], feed_dict={X: images})\n",
" total_train_costs.append(cost)\n",
" \n",
" # test\n",
" total_test_costs = []\n",
" for i in range(test_step):\n",
" batch = mnist.test.next_batch(batch_size)\n",
" images = binarize(batch[0]).reshape([batch_size, height, width, channel])\n",
" cost = sess.run(loss, feed_dict={X: images})\n",
" total_test_costs.append(cost)\n",
" \n",
" train_costs.append(np.mean(total_train_costs))\n",
" test_costs.append(np.mean(total_test_costs))\n",
" print('epoch: %d' % epoch, train_costs[-1], test_costs[-1])\n",
" \n",
" # generate samples\n",
" if epoch % 10 == 0 or epoch == total_epoch - 1:\n",
" samples = np.zeros((100, height, width, channel), dtype='float32')\n",
" for i in range(height):\n",
" for j in range(width):\n",
" for k in range(channel):\n",
" next_sample = binarize(sess.run(y_, {X: samples}))\n",
" samples[:, i, j, k] = next_sample[:, i, j, k]\n",
" \n",
" samples = samples.reshape((10, 10, height, width))\n",
" samples = samples.transpose(1, 2, 0, 3)\n",
" samples = samples.reshape((height * 10, width * 10))\n",
" \n",
" toimage(samples, cmin=0.1, cmax=1.0).save('sample/epoch_%d.jpg' % (epoch+1))\n",
"\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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WwZ51DOiWTFFFLbvLa6JdKqWUOq44IxB6hFumCr9geE87UXrJtn1RLJBSSh1/nBEIGadA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1c5f0b663c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(train_costs)\n",
"plt.plot(test_costs)\n",
"plt.legend(['train', 'test'])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x1c5f2c2e048>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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SD+mKYd4T7K7E81j6ZCulKh9+LTvfto0LuzaFYZiLvMK/l3X/MP2m5Tw0DwfK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07TdGvxUnXB5DgOe563BUZBmG+Y5+iTsoz2YIx0u10OgoA7nQ6vViterVbMzDybzZTu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\nFoSejcZwjC/YQac+t40GwUBY3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Z3paGd3gttHtVoN9XodQRAknlHUVUTQaDSwXC7heR66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\nhYWFhYUBS5ItLCwsLCwsLCwsDPw/eBQTXIPaAuYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1c5f26f8be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from PIL import Image\n",
"\n",
"plt.figure(figsize=(12, 6))\n",
"ax = plt.subplot(1, 2, 1)\n",
"ax.set_title('Train Data')\n",
"images = np.zeros((height * 10, width * 10))\n",
"for i in range(10):\n",
" for j in range(10):\n",
" batch = mnist.train.next_batch(1)\n",
" batch = binarize(batch[0]).reshape([height, width])\n",
" images[i*height:(i+1)*height, j*width:(j+1)*width] += batch\n",
" \n",
"plt.imshow(images, cmap='gray')\n",
"\n",
"ax = plt.subplot(1, 2, 2)\n",
"ax.set_title('Sample')\n",
"image = Image.open('sample/epoch_%d.jpg' % (total_epoch)).convert('L')\n",
"arr = np.asarray(image)\n",
"plt.imshow(arr, cmap='gray')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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