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August 2, 2018 21:48
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 268, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"%matplotlib inline\n", | |
"\n", | |
"import os\n", | |
"# running with non gpu singularity container, so commented out the next line to use CPU\n", | |
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n", | |
"os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n", | |
"import tensorflow as tf\n", | |
"tf.set_random_seed(42)\n", | |
"config = tf.ConfigProto()\n", | |
"config.gpu_options.allow_growth = True\n", | |
"session = tf.Session(config=config)\n", | |
" \n", | |
"import keras.backend.tensorflow_backend as K\n", | |
"\n", | |
"import keras\n", | |
"from keras.models import Sequential, Model, load_model\n", | |
"from keras.layers import Dense, Dropout\n", | |
"from keras.layers import LeakyReLU, Lambda\n", | |
"from keras.layers import Input, merge, Concatenate, concatenate, Add, Activation\n", | |
"from keras.losses import binary_crossentropy\n", | |
"from keras.datasets import mnist\n", | |
"\n", | |
"import numpy as np\n", | |
"import time\n", | |
"import pickle\n", | |
"import sys\n", | |
"\n", | |
"import matplotlib.pyplot as plt\n", | |
"from matplotlib.colors import LogNorm\n", | |
"\n", | |
"from sklearn.model_selection import train_test_split\n", | |
"\n", | |
"from IPython.display import Image, display\n", | |
"\n", | |
"np.random.seed(42)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 269, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def make_mnist_plots(preds,rows=None,cols=None,fname=None,show=False):\n", | |
" n_examples = preds.shape[0]\n", | |
" imgs = preds.reshape(n_examples, 28, 28)\n", | |
" rows = rows or int(n_examples**0.5)\n", | |
" cols = cols or int(n_examples**0.5)\n", | |
" fig, axs = plt.subplots(rows,cols,figsize=(cols,rows))\n", | |
" for img,ax in zip(imgs,axs.reshape(-1)):\n", | |
" ax.imshow(img,cmap=\"gray_r\")\n", | |
" \n", | |
" def clean(ax):\n", | |
" ax.set_frame_on(False)\n", | |
" ax.get_xaxis().set_visible(False)\n", | |
" ax.get_yaxis().set_visible(False)\n", | |
" map(clean,fig.axes)\n", | |
" # fig.set_tight_layout(True)\n", | |
" fig.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0.05, hspace=0.05)\n", | |
" if fname: fig.savefig(fname)\n", | |
" if show: display(Image(fname))\n", | |
"\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 270, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"class GAN():\n", | |
" def __init__(self, **kwargs):\n", | |
"\n", | |
" self.args = dict(kwargs)\n", | |
"\n", | |
" self.tag = kwargs[\"tag\"]\n", | |
" self.noise_shape = (int(kwargs[\"noise_size\"]),)\n", | |
" self.output_shape = (int(kwargs[\"output_size\"]),)\n", | |
" self.nepochs_dump_pred_metrics = int(kwargs[\"nepochs_dump_pred_metrics\"])\n", | |
" self.nepochs_dump_plots = int(kwargs[\"nepochs_dump_plots\"])\n", | |
" self.nepochs_max = int(kwargs[\"nepochs_max\"])\n", | |
" self.batch_size = int(kwargs[\"batch_size\"])\n", | |
" self.do_soft_labels = kwargs[\"do_soft_labels\"]\n", | |
" self.do_noisy_labels = kwargs[\"do_noisy_labels\"]\n", | |
" self.nepochs_decay_noisy_labels = int(kwargs[\"nepochs_decay_noisy_labels\"])\n", | |
" self.terminate_early = kwargs[\"terminate_early\"]\n", | |
" self.mnist_noise = kwargs.get(\"mnist_noise\",100)\n", | |
" \n", | |
" \n", | |
" os.system(\"mkdir -p ./progress/{}/\".format(self.tag))\n", | |
"\n", | |
" self.data = None\n", | |
" self.d_epochinfo = {}\n", | |
" self.X_train = None\n", | |
"\n", | |
" self.noise_shape = (self.mnist_noise,)\n", | |
" self.output_shape = (784,)\n", | |
" self.optimizer_disc = keras.optimizers.Adam(lr=0.0002, beta_1=0.5)\n", | |
" self.optimizer_gen = keras.optimizers.Adam(lr=0.0002, beta_1=0.5)\n", | |
" \n", | |
" optimizer_d = self.optimizer_disc\n", | |
" optimizer_g = self.optimizer_gen\n", | |
" \n", | |
" self.loss = \"binary_crossentropy\"\n", | |
" \n", | |
" # Build and compile the two independent models\n", | |
" self.generator = self.build_generator()\n", | |
" self.generator.compile(loss=self.loss, optimizer=optimizer_g)\n", | |
"\n", | |
" self.discriminator = self.build_discriminator()\n", | |
" self.discriminator.compile(loss=self.loss, optimizer=optimizer_d, metrics=['accuracy'])\n", | |
" \n", | |
" # Make the combined model\n", | |
" # The combined model (stacked generator and discriminator) takes\n", | |
" # noise as input => generates images => determines validity\n", | |
" self.discriminator.trainable = False #only train the generator\n", | |
" z = Input(shape=self.noise_shape)\n", | |
" img = self.generator(z)\n", | |
" valid = self.discriminator(img)\n", | |
" self.combined = Model(z, valid)\n", | |
" self.combined.compile(loss=self.loss, optimizer=optimizer_g)\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 271, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"class GAN(GAN):\n", | |
" \n", | |
" def build_generator(self):\n", | |
" \n", | |
" inputs = Input(shape=self.noise_shape)\n", | |
"# for iw,w in enumerate([1024,512,784,784]):\n", | |
"# for iw,w in enumerate([700,500,500,500]):\n", | |
" for iw,w in enumerate([700,500,400]):\n", | |
" extra = {}\n", | |
" if iw == 0:\n", | |
" extra = {\"kernel_initializer\":keras.initializers.RandomNormal(stddev=0.02)}\n", | |
" x = Dense(w, **extra)(inputs if iw == 0 else x)\n", | |
" x = LeakyReLU(0.2)(x)\n", | |
" \n", | |
" x = Dense(self.output_shape[0], activation=\"tanh\")(x)\n", | |
" model = Model(inputs=inputs, outputs=x)\n", | |
"\n", | |
"# model.summary()\n", | |
" print \"Generator params: {}\".format(model.count_params())\n", | |
" \n", | |
" return model\n", | |
" \n", | |
" def build_discriminator(self):\n", | |
"\n", | |
" discriminator = Sequential()\n", | |
" \n", | |
" inputs = Input(shape=self.output_shape)\n", | |
"# for iw,w in enumerate([1024,512,256]):\n", | |
"# for iw,w in enumerate([700,500,200]):\n", | |
" for iw,w in enumerate([500,300,100]):\n", | |
" extra = {}\n", | |
" if iw == 0:\n", | |
" extra = {\"kernel_initializer\":keras.initializers.RandomNormal(stddev=0.02)}\n", | |
" x = Dense(w, **extra)(inputs if iw == 0 else x)\n", | |
" x = LeakyReLU(0.2)(x)\n", | |
" x = Dropout(0.3)(x)\n", | |
" \n", | |
" x = Dense(1, activation=\"sigmoid\")(x)\n", | |
" model = Model(inputs=inputs, outputs=x)\n", | |
" \n", | |
"# discriminator.summary()\n", | |
" print \"Discriminator params: {}\".format(model.count_params())\n", | |
" \n", | |
" return model" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 273, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"class GAN(GAN):\n", | |
" \n", | |
" def load_data(self):\n", | |
" if self.data is not None: return\n", | |
" \n", | |
" (self.data, y_train), (X_test, y_test) = mnist.load_data()\n", | |
" self.data = np.array([x.flatten() for x in self.data[:]])\n", | |
"\n", | |
" def get_noise(self, amount=1024):\n", | |
" noise_half = np.random.normal(0, 1, (amount//2, self.noise_shape[0]))\n", | |
" noise_full = np.random.normal(0, 1, (amount, self.noise_shape[0]))\n", | |
" return noise_half, noise_full\n", | |
"\n", | |
" \n", | |
" def train(self):\n", | |
"\n", | |
" self.load_data()\n", | |
" \n", | |
" self.losses = []\n", | |
" \n", | |
" self.X_train = (self.data.astype(np.float32) - 127.5)/127.5\n", | |
"\n", | |
"\n", | |
" # make an alias to save typing\n", | |
" X_train = self.X_train\n", | |
" #print(\"X_train shape:\",X_train.shape)\n", | |
" \n", | |
" half_batch = int(self.batch_size / 2)\n", | |
"\n", | |
" prev_gen_loss = -1\n", | |
" prev_disc_loss = -1\n", | |
" n_loss_same_gen = 0 # number of epochs for which generator loss has remained ~same (within 0.01%)\n", | |
" n_loss_same_disc = 0 # number of epochs for which discriminator loss has remained ~same (within 0.01%)\n", | |
" old_info = -1, -1\n", | |
" \n", | |
" for epoch in range(self.nepochs_max):\n", | |
"\n", | |
" if self.terminate_early:\n", | |
" if n_loss_same_gen > 1000 or n_loss_same_disc > 1000:\n", | |
" print \"BREAKING because disc/gen loss has remained the same for {}/{} epochs!\".format(n_loss_same_disc,n_loss_same_gen)\n", | |
" break\n", | |
"\n", | |
" self.discriminator.trainable = True\n", | |
" \n", | |
" ndisc = 1\n", | |
" for idisc in range(ndisc):\n", | |
" # Select a random half batch of images\n", | |
" idx = np.random.randint(0, X_train.shape[0], half_batch)\n", | |
" imgs = X_train[idx]\n", | |
"\n", | |
" noise_half, noise_full = self.get_noise(self.batch_size)\n", | |
" gen_imgs = self.generator.predict(noise_full)\n", | |
"\n", | |
" ones = np.ones((half_batch, 1))\n", | |
" zeros = np.zeros((half_batch, 1))\n", | |
" if self.do_soft_labels: ones *= 0.9\n", | |
" if self.do_noisy_labels:\n", | |
" frac = 0.3*np.exp(-epoch/self.nepochs_decay_noisy_labels)\n", | |
" if frac > 0.005:\n", | |
" ones[np.random.randint(0, len(ones), int(frac*len(ones)))] = 0\n", | |
" zeros[np.random.randint(0, len(zeros), int(frac*len(zeros)))] = 1\n", | |
"\n", | |
" d_loss = self.discriminator.train_on_batch(np.concatenate([imgs, gen_imgs[:half_batch]]), np.concatenate([ones, zeros]))\n", | |
"\n", | |
" self.discriminator.trainable = False\n", | |
" \n", | |
" # The generator wants the discriminator to label the generated samples\n", | |
" # as valid (ones)\n", | |
" valid_y = np.array([1] * self.batch_size)\n", | |
"\n", | |
" # Train the generator\n", | |
" g_loss = self.combined.train_on_batch(noise_full, valid_y)\n", | |
"\n", | |
" if (g_loss - prev_gen_loss) < 0.0001: n_loss_same_gen += 1\n", | |
" else: n_loss_same_gen = 0\n", | |
" prev_gen_loss = g_loss\n", | |
"\n", | |
" if (d_loss[0] - prev_disc_loss) < 0.0001: n_loss_same_disc += 1\n", | |
" else: n_loss_same_disc = 0\n", | |
" prev_disc_loss = d_loss[0]\n", | |
" \n", | |
" if epoch % self.nepochs_dump_pred_metrics == 0 and epoch > 0:\n", | |
" if \"epoch\" not in self.d_epochinfo:\n", | |
" self.d_epochinfo[\"epoch\"] = []\n", | |
" self.d_epochinfo[\"d_acc\"] = []\n", | |
" self.d_epochinfo[\"d_loss\"] = []\n", | |
" self.d_epochinfo[\"g_loss\"] = []\n", | |
" else:\n", | |
" self.d_epochinfo[\"epoch\"].append(epoch)\n", | |
" self.d_epochinfo[\"d_acc\"].append(100*d_loss[1])\n", | |
" self.d_epochinfo[\"d_loss\"].append(d_loss[0])\n", | |
" self.d_epochinfo[\"g_loss\"].append(g_loss)\n", | |
"\n", | |
" sys.stdout.write(\"\\r{} [D loss: {}, acc.: {:.2f}%] [G loss: {}]\".format(epoch, d_loss[0], 100.0*d_loss[1], g_loss))\n", | |
" if epoch % self.nepochs_dump_plots == 0 and epoch > 0:\n", | |
" fname = \"./progress/{}/gan_generated_image_epoch_{}.png\".format(self.tag, epoch)\n", | |
" make_mnist_plots(gen_imgs[:36], fname=fname,show=True)\n", | |
" \n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 274, | |
"metadata": { | |
"scrolled": true | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Generator params: 935984\n", | |
"Discriminator params: 573001\n" | |
] | |
} | |
], | |
"source": [ | |
"# defaults\n", | |
"params = {\n", | |
" \"output_size\": 784,\n", | |
" \"noise_size\": 100,\n", | |
" \"nepochs_dump_pred_metrics\": 50,\n", | |
" \"nepochs_dump_plots\": 3000,\n", | |
" \"nepochs_max\": 30001,\n", | |
" \"batch_size\": 256,\n", | |
" \"do_soft_labels\": False,\n", | |
" \"do_noisy_labels\": False,\n", | |
" \"terminate_early\": True,\n", | |
" \"nepochs_decay_noisy_labels\": 2000,\n", | |
" }\n", | |
"\n", | |
"# change tag for provenance\n", | |
"params[\"tag\"] = \"mnist_v4_default\"\n", | |
"\n", | |
"# print params\n", | |
"gan = GAN(**params)\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 275, | |
"metadata": { | |
"scrolled": true | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"3000 [D loss: 0.35623550415, acc.: 83.20%] [G loss: 1.95486176014]]]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"6000 [D loss: 0.548330307007, acc.: 71.88%] [G loss: 1.18572974205]]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"9000 [D loss: 0.614158630371, acc.: 64.06%] [G loss: 0.960997879505]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"12000 [D loss: 0.621903061867, acc.: 66.41%] [G loss: 0.99080914259]]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"15000 [D loss: 0.607028543949, acc.: 68.36%] [G loss: 0.91440987587]]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"18000 [D loss: 0.63794285059, acc.: 63.67%] [G loss: 0.926706194878]]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"21000 [D loss: 0.647638201714, acc.: 61.33%] [G loss: 0.771631896496]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"24000 [D loss: 0.640033245087, acc.: 62.89%] [G loss: 1.07105839252]]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"27000 [D loss: 0.609472632408, acc.: 66.80%] [G loss: 0.990878105164]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"30000 [D loss: 0.633771300316, acc.: 64.45%] [G loss: 0.768197655678]" | |
] | |
} | |
], | |
"source": [ | |
"gan.train()\n", | |
"\n", | |
"# params[\"mnist_noise\"] = 100\n", | |
"# for i in [2048,1024,512,256,128,64,32]:\n", | |
"# params[\"batch_size\"] = i\n", | |
"# params[\"tag\"] = \"mnist_scan_v3_bs_%i\" % i\n", | |
"# gan = GAN(**params)\n", | |
"# gan.train()\n", | |
" " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 276, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"['epoch', 'd_loss', 'g_loss', 'd_acc']\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.legend.Legend at 0x7f5d46053550>" | |
] | |
}, | |
"execution_count": 276, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 720x216 with 2 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"info = gan.d_epochinfo\n", | |
"print info.keys()\n", | |
"\n", | |
"def smooth(x,window=31,npoly=2):\n", | |
" from scipy.signal import savgol_filter\n", | |
" return savgol_filter(x,window,npoly)\n", | |
"\n", | |
"fig, (ax1,ax2) = plt.subplots(1,2, figsize=(10,3))\n", | |
"ax1.plot(info[\"epoch\"],info[\"d_loss\"],label=\"d loss (raw)\")\n", | |
"ax1.plot(info[\"epoch\"],smooth(info[\"d_loss\"]),label=\"d loss\")\n", | |
"ax1.plot(info[\"epoch\"],info[\"g_loss\"],label=\"g loss (raw)\")\n", | |
"ax1.plot(info[\"epoch\"],smooth(info[\"g_loss\"]),label=\"g loss\")\n", | |
"# ax1.set_yscale(\"log\",nonposy=\"clip\")\n", | |
"ax1.legend()\n", | |
"ax2.plot(info[\"epoch\"],info[\"d_acc\"], label=\"d acc (raw)\")\n", | |
"ax2.plot(info[\"epoch\"],smooth(info[\"d_acc\"]), label=\"d acc\")\n", | |
"ax2.legend()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 277, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 1584x360 with 110 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"make_mnist_plots(gan.generator.predict(gan.get_noise(110)[1]),rows=5,cols=22)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 2", | |
"language": "python", | |
"name": "python2" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 2 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython2", | |
"version": "2.7.12" | |
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}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 268, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"%matplotlib inline\n", | |
"\n", | |
"import os\n", | |
"# running with non gpu singularity container, so commented out the next line to use CPU\n", | |
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n", | |
"os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n", | |
"import tensorflow as tf\n", | |
"tf.set_random_seed(42)\n", | |
"config = tf.ConfigProto()\n", | |
"config.gpu_options.allow_growth = True\n", | |
"session = tf.Session(config=config)\n", | |
" \n", | |
"import keras.backend.tensorflow_backend as K\n", | |
"\n", | |
"import keras\n", | |
"from keras.models import Sequential, Model, load_model\n", | |
"from keras.layers import Dense, Dropout\n", | |
"from keras.layers import LeakyReLU, Lambda\n", | |
"from keras.layers import Input, merge, Concatenate, concatenate, Add, Activation\n", | |
"from keras.losses import binary_crossentropy\n", | |
"from keras.datasets import mnist\n", | |
"\n", | |
"import numpy as np\n", | |
"import time\n", | |
"import pickle\n", | |
"import sys\n", | |
"\n", | |
"import matplotlib.pyplot as plt\n", | |
"from matplotlib.colors import LogNorm\n", | |
"\n", | |
"from sklearn.model_selection import train_test_split\n", | |
"\n", | |
"from IPython.display import Image, display\n", | |
"\n", | |
"np.random.seed(42)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 269, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def make_mnist_plots(preds,rows=None,cols=None,fname=None,show=False):\n", | |
" n_examples = preds.shape[0]\n", | |
" imgs = preds.reshape(n_examples, 28, 28)\n", | |
" rows = rows or int(n_examples**0.5)\n", | |
" cols = cols or int(n_examples**0.5)\n", | |
" fig, axs = plt.subplots(rows,cols,figsize=(cols,rows))\n", | |
" for img,ax in zip(imgs,axs.reshape(-1)):\n", | |
" ax.imshow(img,cmap=\"gray_r\")\n", | |
" \n", | |
" def clean(ax):\n", | |
" ax.set_frame_on(False)\n", | |
" ax.get_xaxis().set_visible(False)\n", | |
" ax.get_yaxis().set_visible(False)\n", | |
" map(clean,fig.axes)\n", | |
" # fig.set_tight_layout(True)\n", | |
" fig.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0.05, hspace=0.05)\n", | |
" if fname: fig.savefig(fname)\n", | |
" if show: display(Image(fname))\n", | |
"\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 270, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"class GAN():\n", | |
" def __init__(self, **kwargs):\n", | |
"\n", | |
" self.args = dict(kwargs)\n", | |
"\n", | |
" self.tag = kwargs[\"tag\"]\n", | |
" self.noise_shape = (int(kwargs[\"noise_size\"]),)\n", | |
" self.output_shape = (int(kwargs[\"output_size\"]),)\n", | |
" self.nepochs_dump_pred_metrics = int(kwargs[\"nepochs_dump_pred_metrics\"])\n", | |
" self.nepochs_dump_plots = int(kwargs[\"nepochs_dump_plots\"])\n", | |
" self.nepochs_max = int(kwargs[\"nepochs_max\"])\n", | |
" self.batch_size = int(kwargs[\"batch_size\"])\n", | |
" self.do_soft_labels = kwargs[\"do_soft_labels\"]\n", | |
" self.do_noisy_labels = kwargs[\"do_noisy_labels\"]\n", | |
" self.nepochs_decay_noisy_labels = int(kwargs[\"nepochs_decay_noisy_labels\"])\n", | |
" self.terminate_early = kwargs[\"terminate_early\"]\n", | |
" self.mnist_noise = kwargs.get(\"mnist_noise\",100)\n", | |
" \n", | |
" \n", | |
" os.system(\"mkdir -p ./progress/{}/\".format(self.tag))\n", | |
"\n", | |
" self.data = None\n", | |
" self.d_epochinfo = {}\n", | |
" self.X_train = None\n", | |
"\n", | |
" self.noise_shape = (self.mnist_noise,)\n", | |
" self.output_shape = (784,)\n", | |
" self.optimizer_disc = keras.optimizers.Adam(lr=0.0002, beta_1=0.5)\n", | |
" self.optimizer_gen = keras.optimizers.Adam(lr=0.0002, beta_1=0.5)\n", | |
" \n", | |
" optimizer_d = self.optimizer_disc\n", | |
" optimizer_g = self.optimizer_gen\n", | |
" \n", | |
" self.loss = \"binary_crossentropy\"\n", | |
" \n", | |
" # Build and compile the two independent models\n", | |
" self.generator = self.build_generator()\n", | |
" self.generator.compile(loss=self.loss, optimizer=optimizer_g)\n", | |
"\n", | |
" self.discriminator = self.build_discriminator()\n", | |
" self.discriminator.compile(loss=self.loss, optimizer=optimizer_d, metrics=['accuracy'])\n", | |
" \n", | |
" # Make the combined model\n", | |
" # The combined model (stacked generator and discriminator) takes\n", | |
" # noise as input => generates images => determines validity\n", | |
" self.discriminator.trainable = False #only train the generator\n", | |
" z = Input(shape=self.noise_shape)\n", | |
" img = self.generator(z)\n", | |
" valid = self.discriminator(img)\n", | |
" self.combined = Model(z, valid)\n", | |
" self.combined.compile(loss=self.loss, optimizer=optimizer_g)\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 271, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"class GAN(GAN):\n", | |
" \n", | |
" def build_generator(self):\n", | |
" \n", | |
" inputs = Input(shape=self.noise_shape)\n", | |
"# for iw,w in enumerate([1024,512,784,784]):\n", | |
"# for iw,w in enumerate([700,500,500,500]):\n", | |
" for iw,w in enumerate([700,500,400]):\n", | |
" extra = {}\n", | |
" if iw == 0:\n", | |
" extra = {\"kernel_initializer\":keras.initializers.RandomNormal(stddev=0.02)}\n", | |
" x = Dense(w, **extra)(inputs if iw == 0 else x)\n", | |
" x = LeakyReLU(0.2)(x)\n", | |
" \n", | |
" x = Dense(self.output_shape[0], activation=\"tanh\")(x)\n", | |
" model = Model(inputs=inputs, outputs=x)\n", | |
"\n", | |
"# model.summary()\n", | |
" print \"Generator params: {}\".format(model.count_params())\n", | |
" \n", | |
" return model\n", | |
" \n", | |
" def build_discriminator(self):\n", | |
"\n", | |
" discriminator = Sequential()\n", | |
" \n", | |
" inputs = Input(shape=self.output_shape)\n", | |
"# for iw,w in enumerate([1024,512,256]):\n", | |
"# for iw,w in enumerate([700,500,200]):\n", | |
" for iw,w in enumerate([500,300,100]):\n", | |
" extra = {}\n", | |
" if iw == 0:\n", | |
" extra = {\"kernel_initializer\":keras.initializers.RandomNormal(stddev=0.02)}\n", | |
" x = Dense(w, **extra)(inputs if iw == 0 else x)\n", | |
" x = LeakyReLU(0.2)(x)\n", | |
" x = Dropout(0.3)(x)\n", | |
" \n", | |
" x = Dense(1, activation=\"sigmoid\")(x)\n", | |
" model = Model(inputs=inputs, outputs=x)\n", | |
" \n", | |
"# discriminator.summary()\n", | |
" print \"Discriminator params: {}\".format(model.count_params())\n", | |
" \n", | |
" return model" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 273, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"class GAN(GAN):\n", | |
" \n", | |
" def load_data(self):\n", | |
" if self.data is not None: return\n", | |
" \n", | |
" (self.data, y_train), (X_test, y_test) = mnist.load_data()\n", | |
" self.data = np.array([x.flatten() for x in self.data[:]])\n", | |
"\n", | |
" def get_noise(self, amount=1024):\n", | |
" noise_half = np.random.normal(0, 1, (amount//2, self.noise_shape[0]))\n", | |
" noise_full = np.random.normal(0, 1, (amount, self.noise_shape[0]))\n", | |
" return noise_half, noise_full\n", | |
"\n", | |
" \n", | |
" def train(self):\n", | |
"\n", | |
" self.load_data()\n", | |
" \n", | |
" self.losses = []\n", | |
" \n", | |
" self.X_train = (self.data.astype(np.float32) - 127.5)/127.5\n", | |
"\n", | |
"\n", | |
" # make an alias to save typing\n", | |
" X_train = self.X_train\n", | |
" #print(\"X_train shape:\",X_train.shape)\n", | |
" \n", | |
" half_batch = int(self.batch_size / 2)\n", | |
"\n", | |
" prev_gen_loss = -1\n", | |
" prev_disc_loss = -1\n", | |
" n_loss_same_gen = 0 # number of epochs for which generator loss has remained ~same (within 0.01%)\n", | |
" n_loss_same_disc = 0 # number of epochs for which discriminator loss has remained ~same (within 0.01%)\n", | |
" old_info = -1, -1\n", | |
" \n", | |
" for epoch in range(self.nepochs_max):\n", | |
"\n", | |
" if self.terminate_early:\n", | |
" if n_loss_same_gen > 1000 or n_loss_same_disc > 1000:\n", | |
" print \"BREAKING because disc/gen loss has remained the same for {}/{} epochs!\".format(n_loss_same_disc,n_loss_same_gen)\n", | |
" break\n", | |
"\n", | |
" self.discriminator.trainable = True\n", | |
" \n", | |
" ndisc = 1\n", | |
" for idisc in range(ndisc):\n", | |
" # Select a random half batch of images\n", | |
" idx = np.random.randint(0, X_train.shape[0], half_batch)\n", | |
" imgs = X_train[idx]\n", | |
"\n", | |
" noise_half, noise_full = self.get_noise(self.batch_size)\n", | |
" gen_imgs = self.generator.predict(noise_full)\n", | |
"\n", | |
" ones = np.ones((half_batch, 1))\n", | |
" zeros = np.zeros((half_batch, 1))\n", | |
" if self.do_soft_labels: ones *= 0.9\n", | |
" if self.do_noisy_labels:\n", | |
" frac = 0.3*np.exp(-epoch/self.nepochs_decay_noisy_labels)\n", | |
" if frac > 0.005:\n", | |
" ones[np.random.randint(0, len(ones), int(frac*len(ones)))] = 0\n", | |
" zeros[np.random.randint(0, len(zeros), int(frac*len(zeros)))] = 1\n", | |
"\n", | |
" d_loss = self.discriminator.train_on_batch(np.concatenate([imgs, gen_imgs[:half_batch]]), np.concatenate([ones, zeros]))\n", | |
"\n", | |
" self.discriminator.trainable = False\n", | |
" \n", | |
" # The generator wants the discriminator to label the generated samples\n", | |
" # as valid (ones)\n", | |
" valid_y = np.array([1] * self.batch_size)\n", | |
"\n", | |
" # Train the generator\n", | |
" g_loss = self.combined.train_on_batch(noise_full, valid_y)\n", | |
"\n", | |
" if (g_loss - prev_gen_loss) < 0.0001: n_loss_same_gen += 1\n", | |
" else: n_loss_same_gen = 0\n", | |
" prev_gen_loss = g_loss\n", | |
"\n", | |
" if (d_loss[0] - prev_disc_loss) < 0.0001: n_loss_same_disc += 1\n", | |
" else: n_loss_same_disc = 0\n", | |
" prev_disc_loss = d_loss[0]\n", | |
" \n", | |
" if epoch % self.nepochs_dump_pred_metrics == 0 and epoch > 0:\n", | |
" if \"epoch\" not in self.d_epochinfo:\n", | |
" self.d_epochinfo[\"epoch\"] = []\n", | |
" self.d_epochinfo[\"d_acc\"] = []\n", | |
" self.d_epochinfo[\"d_loss\"] = []\n", | |
" self.d_epochinfo[\"g_loss\"] = []\n", | |
" else:\n", | |
" self.d_epochinfo[\"epoch\"].append(epoch)\n", | |
" self.d_epochinfo[\"d_acc\"].append(100*d_loss[1])\n", | |
" self.d_epochinfo[\"d_loss\"].append(d_loss[0])\n", | |
" self.d_epochinfo[\"g_loss\"].append(g_loss)\n", | |
"\n", | |
" sys.stdout.write(\"\\r{} [D loss: {}, acc.: {:.2f}%] [G loss: {}]\".format(epoch, d_loss[0], 100.0*d_loss[1], g_loss))\n", | |
" if epoch % self.nepochs_dump_plots == 0 and epoch > 0:\n", | |
" fname = \"./progress/{}/gan_generated_image_epoch_{}.png\".format(self.tag, epoch)\n", | |
" make_mnist_plots(gen_imgs[:36], fname=fname,show=True)\n", | |
" \n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 274, | |
"metadata": { | |
"scrolled": true | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Generator params: 935984\n", | |
"Discriminator params: 573001\n" | |
] | |
} | |
], | |
"source": [ | |
"# defaults\n", | |
"params = {\n", | |
" \"output_size\": 784,\n", | |
" \"noise_size\": 100,\n", | |
" \"nepochs_dump_pred_metrics\": 50,\n", | |
" \"nepochs_dump_plots\": 3000,\n", | |
" \"nepochs_max\": 30001,\n", | |
" \"batch_size\": 256,\n", | |
" \"do_soft_labels\": False,\n", | |
" \"do_noisy_labels\": False,\n", | |
" \"terminate_early\": True,\n", | |
" \"nepochs_decay_noisy_labels\": 2000,\n", | |
" }\n", | |
"\n", | |
"# change tag for provenance\n", | |
"params[\"tag\"] = \"mnist_v4_default\"\n", | |
"\n", | |
"# print params\n", | |
"gan = GAN(**params)\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 275, | |
"metadata": { | |
"scrolled": true | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"3000 [D loss: 0.35623550415, acc.: 83.20%] [G loss: 1.95486176014]]]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"6000 [D loss: 0.548330307007, acc.: 71.88%] [G loss: 1.18572974205]]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"9000 [D loss: 0.614158630371, acc.: 64.06%] [G loss: 0.960997879505]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"12000 [D loss: 0.621903061867, acc.: 66.41%] [G loss: 0.99080914259]]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"15000 [D loss: 0.607028543949, acc.: 68.36%] [G loss: 0.91440987587]]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"18000 [D loss: 0.63794285059, acc.: 63.67%] [G loss: 0.926706194878]]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"21000 [D loss: 0.647638201714, acc.: 61.33%] [G loss: 0.771631896496]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"24000 [D loss: 0.640033245087, acc.: 62.89%] [G loss: 1.07105839252]]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"27000 [D loss: 0.609472632408, acc.: 66.80%] [G loss: 0.990878105164]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"30000 [D loss: 0.633771300316, acc.: 64.45%] [G loss: 0.768197655678]" | |
] | |
} | |
], | |
"source": [ | |
"gan.train()\n", | |
"\n", | |
"# params[\"mnist_noise\"] = 100\n", | |
"# for i in [2048,1024,512,256,128,64,32]:\n", | |
"# params[\"batch_size\"] = i\n", | |
"# params[\"tag\"] = \"mnist_scan_v3_bs_%i\" % i\n", | |
"# gan = GAN(**params)\n", | |
"# gan.train()\n", | |
" " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 276, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"['epoch', 'd_loss', 'g_loss', 'd_acc']\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.legend.Legend at 0x7f5d46053550>" | |
] | |
}, | |
"execution_count": 276, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 720x216 with 2 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"info = gan.d_epochinfo\n", | |
"print info.keys()\n", | |
"\n", | |
"def smooth(x,window=31,npoly=2):\n", | |
" from scipy.signal import savgol_filter\n", | |
" return savgol_filter(x,window,npoly)\n", | |
"\n", | |
"fig, (ax1,ax2) = plt.subplots(1,2, figsize=(10,3))\n", | |
"ax1.plot(info[\"epoch\"],info[\"d_loss\"],label=\"d loss (raw)\")\n", | |
"ax1.plot(info[\"epoch\"],smooth(info[\"d_loss\"]),label=\"d loss\")\n", | |
"ax1.plot(info[\"epoch\"],info[\"g_loss\"],label=\"g loss (raw)\")\n", | |
"ax1.plot(info[\"epoch\"],smooth(info[\"g_loss\"]),label=\"g loss\")\n", | |
"# ax1.set_yscale(\"log\",nonposy=\"clip\")\n", | |
"ax1.legend()\n", | |
"ax2.plot(info[\"epoch\"],info[\"d_acc\"], label=\"d acc (raw)\")\n", | |
"ax2.plot(info[\"epoch\"],smooth(info[\"d_acc\"]), label=\"d acc\")\n", | |
"ax2.legend()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 277, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 1584x360 with 110 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"make_mnist_plots(gan.generator.predict(gan.get_noise(110)[1]),rows=5,cols=22)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 2", | |
"language": "python", | |
"name": "python2" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 2 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython2", | |
"version": "2.7.12" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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