{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['randint', 'copy', 'maximum', 'uniform', 'choice', 'average', 'minimum', 'concatenate', 'add', 'dot', 'random', 'subtract', 'multiply']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" ] } ], "source": [ "import numpy as np\n", "np.set_printoptions(threshold=np.inf)\n", "import os\n", "import glob\n", "import string\n", "import random\n", "from PIL import Image, ImageDraw, ImageFont, ImageFilter\n", "from random import randint, choice, uniform\n", "from collections import Counter\n", "from keras import models\n", "from keras import layers\n", "from keras import optimizers\n", "from keras import applications\n", "from keras.preprocessing import image\n", "from keras.layers import *\n", "import tensorflow as tf\n", "import keras.backend as K\n", "import matplotlib.pyplot as plt\n", "%pylab inline\n", "import csv" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# 数字没有0,1; 大写没有ORSTU; 小写没有gjlpqy\n", "NUM = \"23456789\"\n", "ALPHA_UP = \"ABCDEFGHIJKLMNPQVWXYZ\"\n", "ALPHA_LOW = \"abcdefhikmnorstuvwxz\"\n", "ALPHA_NUM = NUM + ALPHA_UP + ALPHA_LOW\n", "\n", "# HEIGHT = 42\n", "# WIDTH = 110\n", "HEIGHT = 70\n", "WIDTH = 200\n", "BATCH_SIZE = 128\n", "captcha_len = 5\n", "LEN = len(ALPHA_NUM)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "def get_wavy_line(w = (0, 100),h = (30, 50)):\n", " '''产生波浪线坐标'''\n", " import random\n", " n = 50\n", " x = 0\n", " y = random.randint(h[0],h[1])\n", " flag = random.randint(0,2)\n", " xy = [(x, y)]\n", " while x < w[1]:\n", " temp_y = random.randint(1, 3)\n", " temp_x = random.randint(5, 10)\n", " if flag == 0:\n", " if y + temp_y > h[1]:\n", " y -= temp_y\n", " flag = 1\n", " else:\n", " y += temp_y\n", " else:\n", " if y - temp_y < h[0]:\n", " y += temp_y\n", " flag = 0\n", " else:\n", " y -= temp_y\n", " x = x+temp_x if x+temp_x < w[1] else w[1]\n", " xy.append((x, y))\n", " return xy\n", "def gen_captcha(text, size=(200,70), fonts=['/usr/share/fonts/WindowsFonts/fonts/calibri.ttf'],fill=(0,255), rotate=(-30,30),\n", " line=(0,10), point=(0,500), color=(0,255), bg=(255,255,255)):\n", " img = Image.new(mode='RGB', size=size, color=bg) #\n", " draw = ImageDraw.Draw(im=img, mode='RGB') # im, mode=None\n", " font = ImageFont.truetype(random.choice(fonts), size=random.randint(25, 35)) # font=None, size=10, index=0, encoding=\"\"\n", " def get_char_img(char,font,color,angle,bg):\n", " '''\n", " 生成单个字符图片，随机颜色加随机旋转\n", " \n", " '''\n", " w, h = draw.textsize(char, font=font)\n", "# w, h = ImageDraw.Draw.textsize(char, font=font)\n", " im = Image.new('RGBA',(w,h), color=bg)\n", " ImageDraw.Draw(im).text((0,0), char, font=font, fill=color)\n", " im = im.crop(im.getbbox())\n", " rot = im.rotate(angle,Image.BILINEAR,expand=1)\n", " bg = Image.new('RGBA',rot.size,color=bg)\n", " im = Image.composite(rot, bg, rot)\n", " return im\n", " #draw.text(xy=(20,30),\n", " #text=text,\n", " #fill=tuple([random.randint(fill[0], fill[1]) for _ in range(3)]),\n", " #font=font) #xy, text, fill=None, font=None, anchor=None\n", " char_color = tuple([random.randint(fill[0],fill[1]) for _ in range(3)])\n", " char_imgs = [get_char_img(char, font, color=char_color, angle=random.randint(rotate[0], rotate[1]), bg=bg) for char in text]\n", " ws = [img.size[0] for img in char_imgs]\n", " hs = [img.size[1] for img in char_imgs]\n", " max_w = max(sum(ws), size[0])\n", " max_h = max(max(hs), size[1]) # 此处之前用错 sum(hs)\n", " if max_w>size[0] or max_h>size[1]:\n", " img = img.resize((max_w, max_h), Image.BILINEAR)\n", " draw = ImageDraw.Draw(im=img, mode='RGB') # im, mode=None\n", " size = img.size\n", "\n", "# assert sum(ws) < size[0]\n", "# assert max(hs) < size[1]\n", " if int((max_w-sum(ws))/5)>=int((max_w-sum(ws))/2):\n", " print('w,h,sum(ws)',max_w,max_h,sum(ws))\n", " temp_x = random.randint(int((max_w-sum(ws))/5)-1, int((max_w-sum(ws))/2))\n", " for i in range(len(char_imgs)):\n", " img.paste(char_imgs[i], box=(temp_x, random.randint(int((size[1]-hs[i])/4)-1, int((size[1]-hs[i])/2)))) #im, box=None, mask=None\n", " temp_x += random.randint(int(ws[i]*0.8)-1, int(ws[i]*0.9))\n", " import copy \n", " img2 = copy.deepcopy(img)\n", " draw = ImageDraw.Draw(im=img2, mode='RGB') # im, mode=None \n", " \n", " for i in range(random.randint(line[0], line[1])):\n", " draw.line(xy=([(random.randint(0, size[0]), random.randint(0, size[1])) for _ in range(2)]),\n", " fill=tuple([random.randint(color[0], color[1]) for _ in range(3)]),\n", " width=random.randint(0,2)) # xy, fill=None, width=0\n", " for i in range(random.randint(point[0], point[1])):\n", " draw.point(xy=(random.randint(0, size[0]), random.randint(0, size[1])),\n", " fill=tuple([random.randint(color[0], color[1]) for _ in range(3)])) # xy, fill=None\n", " for _ in range(random.randint(0, 2)):\n", " draw.line(xy=get_wavy_line(w = (0, 200),h = (50, 70)), \n", " fill=tuple([random.randint(color[0], color[1]) for _ in range(3)]), width=random.randint(1,3))\n", "# return img.resize((200,70), Image.BILINEAR)\n", " return img.resize((200,70), Image.BILINEAR), img2.resize((200,70), Image.BILINEAR)\n", "\n", "fonts_list = glob.glob('/usr/share/fonts/WindowsFonts/fonts/*.ttf')\n", "img, img2 = gen_captcha('GJBIL', fonts=fonts_list, fill=(0,255), rotate=(-20,20),\n", " line=(0,20), point=(0,500), color=(0,255), bg=tuple([random.randint(0,255) for _ in range(3)]))\n", "def gen_one(captcha=''): \n", " image = Image.new('L', (WIDTH, HEIGHT))\n", " gen_characters = random.choice([string.ascii_lowercase, string.ascii_uppercase, string.digits + string.ascii_uppercase])\n", "# print(gen_characters)\n", " if captcha == \"\":\n", " captcha = ''.join([random.choice(list(gen_characters)) for _ in range(captcha_len)])\n", "# if captcha == '':\n", "# captcha = ''.join([choice(list(ALPHA_NUM)) for _ in range(captcha_len)])\n", " img, img2 = gen_captcha(captcha, fonts=fonts_list, fill=(0,255), rotate=(-20,20),\n", " line=(0,20), point=(0,500), color=(0,255), bg=tuple([random.randint(0,255) for _ in range(3)]))\n", "# return np.array(img), np.array(img2)\n", " return np.expand_dims(np.array(img.convert('L')), axis=-1), np.expand_dims(np.array(img2.convert('L')), axis=-1)\n", "def synth_generator():\n", " arrs = []\n", " arrs2 = []\n", " while True:\n", " for _ in range(BATCH_SIZE):\n", " arrs.append(gen_one()[0])\n", " arrs2.append(gen_one()[1])\n", "# yield np.array(arrs)\n", " yield np.array(arrs),np.array(arrs2)\n", " arrs = []\n", " arrs2 = []\n", "def get_image_batch(generator):\n", " \"\"\"keras generators may generate an incomplete batch for the last batch\"\"\"\n", " #img_batch = generator.next()\n", " img_batch = next(generator)\n", " if len(img_batch) != BATCH_SIZE:\n", "# img_batch = generator.next()\n", " img_batch = next(generator)\n", " \n", " assert len(img_batch[0]) == BATCH_SIZE\n", "\n", " return img_batch" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'T'" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# img, img2 = gen_one()\n", "# # print(img.shape)\n", "# # print(im[i][:,:,:1].shape)\n", "# # im = [img, img2]\n", "# # plt.figure(figsize=(50,10))\n", "# # for i in range(2):\n", "# # plt.subplot(1,2,i+1)\n", "# # plt.imshow(im[i][:,:,:1])\n", "# # plt.show()\n", "# real_data, refind = get_image_batch(synth_generator())\n", "# print(real_data.shape, refind.shape)\n", "import random\n", "# from random import randint, choice, uniform\n", "random.choice('ABCDEFGHIJKLMNOPQRSTUVWXYZ')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/keras/engine/training.py:973: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n", " 'Discrepancy between trainable weights and collected trainable'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "combine_loss [22678.094, 22677.4, 0.69314736]\n", "combine_loss [22676.355, 22675.662, 0.69314736]\n", "combine_loss [22356.842, 22356.148, 0.69314736]\n", "combine_loss [22077.504, 22076.811, 0.69314736]\n", 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0.69314736]\n", "combine_loss [22530.346, 22529.652, 0.69314736]\n", "combine_loss [23584.07, 23583.377, 0.69314736]\n", "combine_loss [21052.137, 21051.443, 0.69314736]\n", "combine_loss [22921.979, 22921.285, 0.69314736]\n", "combine_loss [23405.826, 23405.133, 0.69314736]\n", "combine_loss [22445.344, 22444.65, 0.69314736]\n", "combine_loss [23109.742, 23109.049, 0.69314736]\n", "combine_loss [22998.371, 22997.678, 0.69314736]\n", "combine_loss [22381.137, 22380.443, 0.69314736]\n", "combine_loss [22167.137, 22166.443, 0.69314736]\n", "combine_loss [22323.016, 22322.322, 0.69314736]\n", "combine_loss [23719.252, 23718.559, 0.69314736]\n", "combine_loss [22282.305, 22281.611, 0.69314736]\n", "combine_loss [21632.322, 21631.629, 0.69314736]\n", "combine_loss [22827.252, 22826.559, 0.69314736]\n", "combine_loss [22517.197, 22516.504, 0.69314736]\n", "combine_loss [21573.463, 21572.77, 0.69314736]\n", "combine_loss [23053.311, 23052.617, 0.69314736]\n", "combine_loss [23059.676, 23058.982, 0.69314736]\n", "combine_loss [22374.152, 22373.459, 0.69314736]\n", "combine_loss [22512.455, 22511.762, 0.69314736]\n", "combine_loss [23193.381, 23192.688, 0.69314736]\n", "combine_loss [22833.213, 22832.52, 0.69314736]\n", "combine_loss [23730.455, 23729.762, 0.69314736]\n", "combine_loss [23277.789, 23277.096, 0.69314736]\n", "combine_loss [23399.557, 23398.863, 0.69314736]\n", "combine_loss [22932.262, 22931.568, 0.69314736]\n", "combine_loss [22053.596, 22052.902, 0.69314736]\n", "combine_loss [22134.695, 22134.002, 0.69314736]\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0md_loss\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m 1881\u001b[0m \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1882\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1883\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1884\u001b[0m \u001b[0;32mif\u001b[0m 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1349\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m run_metadata)\n\u001b[0m\u001b[1;32m 1351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1352\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "\n", "for i in range(50000): \n", " real_data, gen_data = get_image_batch(synth_generator())\n", "# print(real_data[BATCH_SIZE*i:BATCH_SIZE*(i+1),].shape)\n", " synthetic_image_batch = gen_data\n", "\n", " real_image_batch = real_data\n", "# print(synthetic_image_batch.shape)\n", " y_real = np.array([[[1.0, 0.0]] * discriminator_model.output_shape[1]] * BATCH_SIZE)\n", "\n", " g = refiner_model.train_on_batch(synthetic_image_batch, real_image_batch)\n", "\n", " d = discriminator_model.train_on_batch(real_image_batch, y_real)\n", "\n", " refiner_model_output = refiner_model.predict_on_batch(synthetic_image_batch) \n", " y_refined = np.array([[[0.0, 1.0]] * discriminator_model.output_shape[1]] * BATCH_SIZE)\n", "\n", " d2 = discriminator_model.train_on_batch(refiner_model_output, y_refined)\n", " d_loss = 0.5*np.add(d, d2)\n", "\n", " combine = combined_model.train_on_batch(synthetic_image_batch, [real_image_batch, y_real])\n", "\n", "# print('gan_loss:',g)\n", "# print('disc_loss',d_loss)\n", " print('combine_loss',combine)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def _draw_character(c, draw, angle):\n", "# if c in \"Q\":\n", "# FONT_PATH = './font/Merriweather-Italic.ttf'\n", "# else:\n", "# FONT_PATH = './font/NotoSerif-Italic.ttf'\n", " FONT_PATH = '/usr/share/fonts/WindowsFonts/fonts/arial.ttf'\n", " font_size = 40\n", " font = ImageFont.truetype(FONT_PATH, font_size)\n", " w, h = draw.textsize(c, font=font)\n", " \n", " dx = randint(0, 4)\n", " dy = randint(0, 6)\n", "# dx = dy = 0\n", "\n", " im = Image.new('L', (w+dx, h+dy))\n", " imdraw = ImageDraw.Draw(im)\n", " imdraw.text((dx, dy), c, font=font, fill=255)\n", " \n", " # rotate\n", " #im = im.crop(im.getbbox())\n", " im = im.rotate(angle, Image.BICUBIC, expand=1)\n", " \n", " # warp\n", " dx = w * random.uniform(0.1, 0.3)\n", " dy = h * random.uniform(0.2, 0.3)\n", " x1 = int(random.uniform(-dx, dx))\n", " y1 = int(random.uniform(-dy, dy))\n", " x2 = int(random.uniform(-dx, dx))\n", " y2 = int(random.uniform(-dy, dy))\n", " w2 = w + abs(x1) + abs(x2)\n", " h2 = h + abs(y1) + abs(y2)\n", " data = (\n", " x1, y1,\n", " -x1, h2 - y2,\n", " w2 + x2, h2 + y2,\n", " w2 - x2, -y1,\n", " )\n", " im = im.resize((w2, h2))\n", " im = im.transform((w, h), Image.QUAD, data)\n", " \n", " return im\n", "\n", "def gen_one(captcha=''): \n", " image = Image.new('L', (WIDTH, HEIGHT))\n", " if captcha == '':\n", " captcha = ''.join([choice(list(ALPHA_NUM)) for _ in range(captcha_len)])\n", " \n", " draw = ImageDraw.Draw(image)\n", " images = []\n", " angle = uniform(-20, 20)\n", " for c in captcha:\n", " angle += randint(-2, 2)\n", " images.append(_draw_character(c, draw, angle))\n", " \n", " text_width = sum([im.size[0] for im in images])\n", " width = max(text_width, WIDTH)\n", " text_height = max([im.size[1] for im in images])\n", " height = max(text_height, HEIGHT)\n", " image = image.resize((width, height))\n", " \n", " average = int(text_width / captcha_len)\n", " rand = int(0.1 * average)\n", " offset = randint(int(average * 0.8), int(average * 1.4))\n", " \n", " locations = []\n", " for im in images:\n", " w, h = im.size\n", " location = ((offset, int((height - h) / 2)+randint(-2, 2)))\n", " locations.append(location)\n", " image.paste(im, location, im)\n", " offset = offset + w + random.randint(-rand, 2*rand)\n", " \n", " #print(captcha, locations)\n", " \n", " if width > WIDTH or height > HEIGHT:\n", " image = image.resize((WIDTH, HEIGHT))\n", " \n", " image = image.point(lambda x: 255 if x <= 50 else 0, \"1\")\n", " image_arr = np.array(image)\n", " image_arr = np.expand_dims(image_arr, axis=2).astype(float)\n", " image_arr = np.random.random(size=(HEIGHT, WIDTH, 1)) * image_arr\n", " image_arr = (image_arr > 0.0).astype(float)\n", " return image_arr, captcha\n", "\n", "def synth_generator():\n", " arrs = []\n", " while True:\n", " for _ in range(BATCH_SIZE):\n", " arrs.append(gen_one()[0])\n", " yield np.array(arrs)\n", " arrs = []" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def get_image_batch(generator):\n", " \"\"\"keras generators may generate an incomplete batch for the last batch\"\"\"\n", " #img_batch = generator.next()\n", " img_batch = next(generator)\n", " if len(img_batch) != BATCH_SIZE:\n", " img_batch = generator.next()\n", "\n", " assert len(img_batch) == BATCH_SIZE\n", "\n", " return img_batch" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Real data generator\n", "\n", "# datagen = image.ImageDataGenerator(\n", "# preprocessing_function=applications.xception.preprocess_input\n", "# )\n", "\n", "# flow_from_directory_params = {'target_size': (HEIGHT, WIDTH),\n", "# 'color_mode': 'grayscale',\n", "# 'class_mode': None,\n", "# 'batch_size': BATCH_SIZE}\n", "\n", "# real_generator = datagen.flow_from_directory(\n", "# directory=\"image\",\n", "# **flow_from_directory_params\n", "# )" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py:24: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\", padding=\"same\")`\n", "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py:14: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), padding=\"same\")`\n", " \n", "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py:16: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), padding=\"same\")`\n", " app.launch_new_instance()\n", "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py:32: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1, (1, 1), activation=\"tanh\", padding=\"same\")`\n", "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py:53: UserWarning: Update your `Model` call to the Keras 2 API: `Model(outputs=Tensor(\"co..., name=\"refiner\", inputs=Tensor(\"in...)`\n", "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py:39: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(96, (3, 3), activation=\"relu\", padding=\"same\", strides=(2, 2))`\n", "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py:40: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\", padding=\"same\", strides=(2, 2))`\n", "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py:41: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(pool_size=(3, 3), padding=\"same\", strides=(1, 1))`\n", "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py:42: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\", strides=(1, 1))`\n", "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py:43: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (1, 1), activation=\"relu\", padding=\"same\", strides=(1, 1))`\n", "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py:44: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(2, (1, 1), activation=\"relu\", padding=\"same\", strides=(1, 1))`\n", "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py:59: UserWarning: Update your `Model` call to the Keras 2 API: `Model(outputs=Tensor(\"re..., name=\"discriminator\", inputs=Tensor(\"in...)`\n", "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py:65: UserWarning: Update your `Model` call to the Keras 2 API: `Model(outputs=[:78: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "\n", "Future major versions of TensorFlow will allow gradients to flow\n", "into the labels input on backprop by default.\n", "\n", "See @{tf.nn.softmax_cross_entropy_with_logits_v2}.\n", "\n" ] } ], "source": [ "def refiner_network(input_image_tensor):\n", " \"\"\"\n", " :param input_image_tensor: Input tensor that corresponds to a synthetic image.\n", " :return: Output tensor that corresponds to a refined synthetic image.\n", " \"\"\"\n", " def resnet_block(input_features, nb_features=64, nb_kernel_rows=3, nb_kernel_cols=3):\n", " \"\"\"\n", " A ResNet block with two `nb_kernel_rows` x `nb_kernel_cols` convolutional layers,\n", " each with `nb_features` feature maps.\n", " See Figure 6 in https://arxiv.org/pdf/1612.07828v1.pdf.\n", " :param input_features: Input tensor to ResNet block.\n", " :return: Output tensor from ResNet block.\n", " \"\"\"\n", " y = layers.Convolution2D(nb_features, nb_kernel_rows, nb_kernel_cols, border_mode='same')(input_features)\n", " y = layers.Activation('relu')(y)\n", " y = layers.Convolution2D(nb_features, nb_kernel_rows, nb_kernel_cols, border_mode='same')(y)\n", "\n", " #y = layers.merge([input_features, y], mode='sum')\n", " #y = layers.add([input_features, y])\n", " y = layers.Add()([input_features, y])\n", " return layers.Activation('relu')(y)\n", "\n", " # an input image of size w × h is convolved with 3 × 3 filters that output 64 feature maps\n", " x = layers.Convolution2D(64, 3, 3, border_mode='same', activation='relu')(input_image_tensor)\n", "\n", " # the output is passed through 4 ResNet blocks\n", " for _ in range(4):\n", " x = resnet_block(x)\n", "\n", " # the output of the last ResNet block is passed to a 1 × 1 convolutional layer producing 1 feature map\n", " # corresponding to the refined synthetic image\n", " return layers.Convolution2D(1, 1, 1, border_mode='same', activation='tanh')(x)\n", "\n", "def discriminator_network(input_image_tensor):\n", " \"\"\"\n", " :param input_image_tensor: Input tensor corresponding to an image, either real or refined.\n", " :return: Output tensor that corresponds to the probability of whether an image is real or refined.\n", " \"\"\"\n", " x = layers.Convolution2D(96, 3, 3, border_mode='same', subsample=(2, 2), activation='relu')(input_image_tensor)\n", " x = layers.Convolution2D(64, 3, 3, border_mode='same', subsample=(2, 2), activation='relu')(x)\n", " x = layers.MaxPooling2D(pool_size=(3, 3), border_mode='same', strides=(1, 1))(x)\n", " x = layers.Convolution2D(32, 3, 3, border_mode='same', subsample=(1, 1), activation='relu')(x)\n", " x = layers.Convolution2D(32, 1, 1, border_mode='same', subsample=(1, 1), activation='relu')(x)\n", " x = layers.Convolution2D(2, 1, 1, border_mode='same', subsample=(1, 1), activation='relu')(x)\n", "\n", " # here one feature map corresponds to `is_real` and the other to `is_refined`,\n", " # and the custom loss function is then `tf.nn.sparse_softmax_cross_entropy_with_logits`\n", " return layers.Reshape((-1, 2))(x)\n", "\n", "# Refiner\n", "synthetic_image_tensor = layers.Input(shape=(HEIGHT, WIDTH, 1))\n", "refined_image_tensor = refiner_network(synthetic_image_tensor)\n", "refiner_model = models.Model(input=synthetic_image_tensor, output=refined_image_tensor, name='refiner')\n", "\n", "# Discriminator\n", "refined_or_real_image_tensor = layers.Input(shape=(HEIGHT, WIDTH, 1))\n", "discriminator_output = discriminator_network(refined_or_real_image_tensor)\n", "discriminator_model = models.Model(input=refined_or_real_image_tensor, output=discriminator_output,\n", " name='discriminator')\n", "\n", "# Combined\n", "refiner_model_output = refiner_model(synthetic_image_tensor)\n", "combined_output = discriminator_model(refiner_model_output)\n", "combined_model = models.Model(input=synthetic_image_tensor, output=[refiner_model_output, combined_output],\n", " name='combined')\n", "\n", "def self_regularization_loss(y_true, y_pred):\n", " delta = 0.0001 # FIXME: need to figure out an appropriate value for this\n", " return tf.multiply(delta, tf.reduce_sum(tf.abs(y_pred - y_true)))\n", "\n", "# define custom local adversarial loss (softmax for each image section) for the discriminator\n", "# the adversarial loss function is the sum of the cross-entropy losses over the local patches\n", "def local_adversarial_loss(y_true, y_pred):\n", " # y_true and y_pred have shape (batch_size, # of local patches, 2), but really we just want to average over\n", " # the local patches and batch size so we can reshape to (batch_size * # of local patches, 2)\n", " y_true = tf.reshape(y_true, (-1, 2))\n", " y_pred = tf.reshape(y_pred, (-1, 2))\n", " loss = tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_pred)\n", "\n", " return tf.reduce_mean(loss)\n", "\n", "def wasserstein_loss(y_true, y_pred):\n", " return K.mean(y_true*y_pred)\n", "\n", "\n", "# compile models\n", "#sgd = optimizers.RMSprop()\n", "sgd = optimizers.Adam(lr=2e-4)\n", "\n", "refiner_model.compile(optimizer=sgd, loss=self_regularization_loss)\n", "discriminator_model.trainable = False\n", "combined_model.compile(optimizer=sgd, loss=[self_regularization_loss, local_adversarial_loss])\n", "discriminator_model.trainable = True\n", "discriminator_model.compile(optimizer=sgd, loss=local_adversarial_loss)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# synthetic_image_batch = get_image_batch(synth_generator())\n", "# plt.imshow(synthetic_image_batch[0, :, :, 0])\n", "def get_real_gen_data():\n", " import glob\n", " paths = 'FileInfo0508/*.jpg'\n", " real_data = []\n", " gen_data = []\n", " labels = []\n", " for path in glob.glob(paths)[:1024]:\n", " img = Image.open(path)\n", " arr = np.array(img.convert('L'))\n", " arr = np.expand_dims(arr, axis=2)\n", " real_data.append(arr)\n", " label = path.split('_')[-1][:-4].lower().replace('1','l')\n", " labels.append(label)\n", " gen_data.append(gen_one(label)[0])\n", " # arr = np.expand_dims(arr, axis=0)\n", " return np.array(real_data), np.array(gen_data)\n", "\n", "\n", "# plt.imshow(image_arr[:,:,0])\n", "# print(captcha)\n", "\n" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] }, { "ename": "ResourceExhaustedError", "evalue": "OOM when allocating tensor with shape[128,64,70,200] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv2d_4/convolution = Conv2D[T=DT_FLOAT, _class=[\"loc:@training/Adam/gradients/conv2d_4/convolution_grad/Conv2DBackpropInput\"], data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"SAME\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](activation_2/Relu, conv2d_4/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: loss/mul/_203 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1131_loss/mul\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\nCaused by op 'conv2d_4/convolution', defined at:\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/runpy.py\", line 170, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in \n app.launch_new_instance()\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n app.start()\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 497, in start\n self.io_loop.start()\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 132, in start\n self.asyncio_loop.run_forever()\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/asyncio/base_events.py\", line 301, in run_forever\n self._run_once()\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/asyncio/base_events.py\", line 1198, in _run_once\n handle._run()\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/asyncio/events.py\", line 125, in _run\n self._callback(*self._args)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 122, in _handle_events\n handler_func(fileobj, events)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n return fn(*args, **kwargs)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n self._handle_recv()\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n self._run_callback(callback, msg)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n callback(*args, **kwargs)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n return fn(*args, **kwargs)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n return self.dispatch_shell(stream, msg)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n handler(stream, idents, msg)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n user_expressions, allow_stdin)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2662, in run_cell\n raw_cell, store_history, silent, shell_futures)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2785, in _run_cell\n interactivity=interactivity, compiler=compiler, result=result)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_ast_nodes\n if self.run_code(code, result):\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2961, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"\", line 52, in \n refined_image_tensor = refiner_network(synthetic_image_tensor)\n File \"\", line 28, in refiner_network\n x = resnet_block(x)\n File \"\", line 14, in resnet_block\n y = layers.Convolution2D(nb_features, nb_kernel_rows, nb_kernel_cols, border_mode='same')(input_features)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/keras/engine/topology.py\", line 619, in __call__\n output = self.call(inputs, **kwargs)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/keras/layers/convolutional.py\", line 168, in call\n dilation_rate=self.dilation_rate)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py\", line 3335, in conv2d\n data_format=tf_data_format)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py\", line 780, in convolution\n return op(input, filter)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py\", line 868, in __call__\n return self.conv_op(inp, filter)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py\", line 520, in __call__\n return self.call(inp, filter)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py\", line 204, in __call__\n name=self.name)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py\", line 956, in conv2d\n data_format=data_format, dilations=dilations, name=name)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n op_def=op_def)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py\", line 454, in new_func\n return func(*args, **kwargs)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 3155, in create_op\n op_def=op_def)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 1717, in __init__\n self._traceback = tf_stack.extract_stack()\n\nResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[128,64,70,200] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv2d_4/convolution = Conv2D[T=DT_FLOAT, _class=[\"loc:@training/Adam/gradients/conv2d_4/convolution_grad/Conv2DBackpropInput\"], data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"SAME\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](activation_2/Relu, conv2d_4/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: loss/mul/_203 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1131_loss/mul\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mResourceExhaustedError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1277\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1278\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1279\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1262\u001b[0m return self._call_tf_sessionrun(\n\u001b[0;32m-> 1263\u001b[0;31m options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m 1264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m 1349\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m run_metadata)\n\u001b[0m\u001b[1;32m 1351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mResourceExhaustedError\u001b[0m: OOM when allocating tensor with shape[128,64,70,200] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv2d_4/convolution = Conv2D[T=DT_FLOAT, _class=[\"loc:@training/Adam/gradients/conv2d_4/convolution_grad/Conv2DBackpropInput\"], data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"SAME\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](activation_2/Relu, conv2d_4/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: loss/mul/_203 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1131_loss/mul\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mResourceExhaustedError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0my_real\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdiscriminator_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrefiner_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msynthetic_image_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic_image_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiscriminator_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_image_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_real\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m 1881\u001b[0m \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1882\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1883\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1884\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1885\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 2476\u001b[0m \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2477\u001b[0m updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[0;32m-> 2478\u001b[0;31m **self.session_kwargs)\n\u001b[0m\u001b[1;32m 2479\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 875\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 876\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 877\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 878\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 879\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1099\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1100\u001b[0;31m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m 1101\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1102\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1270\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1271\u001b[0m return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1272\u001b[0;31m run_metadata)\n\u001b[0m\u001b[1;32m 1273\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1274\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1290\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1291\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1293\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mResourceExhaustedError\u001b[0m: OOM when allocating tensor with shape[128,64,70,200] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv2d_4/convolution = Conv2D[T=DT_FLOAT, _class=[\"loc:@training/Adam/gradients/conv2d_4/convolution_grad/Conv2DBackpropInput\"], data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"SAME\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](activation_2/Relu, conv2d_4/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: loss/mul/_203 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1131_loss/mul\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\nCaused by op 'conv2d_4/convolution', defined at:\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/runpy.py\", line 170, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in \n app.launch_new_instance()\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n app.start()\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 497, in start\n self.io_loop.start()\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 132, in start\n self.asyncio_loop.run_forever()\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/asyncio/base_events.py\", line 301, in run_forever\n self._run_once()\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/asyncio/base_events.py\", line 1198, in _run_once\n handle._run()\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/asyncio/events.py\", line 125, in _run\n self._callback(*self._args)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/platform/asyncio.py\", line 122, in _handle_events\n handler_func(fileobj, events)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n return fn(*args, **kwargs)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n self._handle_recv()\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n self._run_callback(callback, msg)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n callback(*args, **kwargs)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n return fn(*args, **kwargs)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n return self.dispatch_shell(stream, msg)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n handler(stream, idents, msg)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n user_expressions, allow_stdin)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2662, in run_cell\n raw_cell, store_history, silent, shell_futures)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2785, in _run_cell\n interactivity=interactivity, compiler=compiler, result=result)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_ast_nodes\n if self.run_code(code, result):\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2961, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"\", line 52, in \n refined_image_tensor = refiner_network(synthetic_image_tensor)\n File \"\", line 28, in refiner_network\n x = resnet_block(x)\n File \"\", line 14, in resnet_block\n y = layers.Convolution2D(nb_features, nb_kernel_rows, nb_kernel_cols, border_mode='same')(input_features)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/keras/engine/topology.py\", line 619, in __call__\n output = self.call(inputs, **kwargs)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/keras/layers/convolutional.py\", line 168, in call\n dilation_rate=self.dilation_rate)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py\", line 3335, in conv2d\n data_format=tf_data_format)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py\", line 780, in convolution\n return op(input, filter)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py\", line 868, in __call__\n return self.conv_op(inp, filter)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py\", line 520, in __call__\n return self.call(inp, filter)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py\", line 204, in __call__\n name=self.name)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py\", line 956, in conv2d\n data_format=data_format, dilations=dilations, name=name)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py\", line 787, in _apply_op_helper\n op_def=op_def)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py\", line 454, in new_func\n return func(*args, **kwargs)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 3155, in create_op\n op_def=op_def)\n File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\", line 1717, in __init__\n self._traceback = tf_stack.extract_stack()\n\nResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[128,64,70,200] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv2d_4/convolution = Conv2D[T=DT_FLOAT, _class=[\"loc:@training/Adam/gradients/conv2d_4/convolution_grad/Conv2DBackpropInput\"], data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"SAME\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](activation_2/Relu, conv2d_4/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: loss/mul/_203 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1131_loss/mul\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n" ] } ], "source": [ "for epoch in range(2000):\n", "# real_data, gen_data = get_real_gen_data()\n", " real_data, gen_data = get_image_batch(synth_generator())\n", " step = len(real_data)//BATCH_SIZE\n", " print(step)\n", " for i in range(step): \n", "# print(real_data[BATCH_SIZE*i:BATCH_SIZE*(i+1),].shape)\n", " synthetic_image_batch = gen_data[BATCH_SIZE*i:BATCH_SIZE*(i+1),]\n", " \n", " real_image_batch = real_data[BATCH_SIZE*i:BATCH_SIZE*(i+1),]\n", "# print(synthetic_image_batch.shape)\n", " y_real = np.array([[[1.0, 0.0]] * discriminator_model.output_shape[1]] * BATCH_SIZE)\n", "\n", " g = refiner_model.train_on_batch(synthetic_image_batch, synthetic_image_batch)\n", "\n", " d = discriminator_model.train_on_batch(real_image_batch, y_real)\n", "\n", " refiner_model_output = refiner_model.predict_on_batch(synthetic_image_batch) \n", " y_refined = np.array([[[0.0, 1.0]] * discriminator_model.output_shape[1]] * BATCH_SIZE)\n", "\n", " d2 = discriminator_model.train_on_batch(refiner_model_output, y_refined)\n", " d_loss = 0.5*np.add(d, d2)\n", "\n", " combine = combined_model.train_on_batch(synthetic_image_batch, [real_image_batch, y_real])\n", "\n", " print('gan_loss:',g)\n", " print('disc_loss',d_loss)\n", " print('combine_loss',combine)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(70, 200)\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "synthetic_image_batch, _ = gen_one('lasdi')\n", "arr = refiner_model.predict_on_batch(np.expand_dims(synthetic_image_batch, axis=0))\n", "# arr = np.expand_dims(gen_one('ajidl')[0])\n", "plt.imshow(arr[0, :, :, 0])\n", "print(arr[0, :, :, 0].shape)" ] } ], "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": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 2 }