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amrahsmaytas/nasnet_fine_tuning_8clf_0.8850_0.9740.ipynb

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Created October 26, 2020 05:02
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nasnet_fine_tuning_8clf_0.8850_0.9740.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:23:15.079234Z",
"start_time": "2018-04-07T17:23:14.228909Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import os\n",
"os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'\n",
"n_gpus = len(os.environ['CUDA_VISIBLE_DEVICES'].split(','))\n",
"model_name = 'nasnet_fine_tuning_8clf_2'\n",
"\n",
"import keras.backend as K\n",
"import tensorflow as tf\n",
"\n",
"# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)\n",
"# sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 导入必要的库"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:23:15.316079Z",
"start_time": "2018-04-07T17:23:15.080390Z"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from keras.layers import *\n",
"from keras.models import *\n",
"from keras.optimizers import *\n",
"from keras.applications import *\n",
"from keras.regularizers import l2\n",
"\n",
"from keras.preprocessing.image import *\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import random\n",
"import os\n",
"import cv2\n",
"from tqdm import tqdm\n",
"from glob import glob\n",
"import multiprocessing\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from collections import Counter\n",
"from keras import backend as K\n",
"from keras.utils import multi_gpu_model\n",
"\n",
"from IPython.display import display, Image\n",
"\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 读取数据集"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:23:15.472275Z",
"start_time": "2018-04-07T17:23:15.317186Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>filename</th>\n",
" <th>label_name</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Images/skirt_length_labels/0575dc5183059223924...</td>\n",
" <td>skirt_length</td>\n",
" <td>nynnnn</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Images/collar_design_labels/ba453aef46c484d4f3...</td>\n",
" <td>collar_design</td>\n",
" <td>nnynn</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Images/coat_length_labels/ac448ded8b3ebd88a998...</td>\n",
" <td>coat_length</td>\n",
" <td>nnnnnynn</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Images/coat_length_labels/206b38af4f2d55393865...</td>\n",
" <td>coat_length</td>\n",
" <td>nnnnnynn</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Images/pant_length_labels/7da34d442a8dcc7c84a7...</td>\n",
" <td>pant_length</td>\n",
" <td>nnynnn</td>\n",
" </tr>\n",
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],
"text/plain": [
" filename label_name label\n",
"0 Images/skirt_length_labels/0575dc5183059223924... skirt_length nynnnn\n",
"1 Images/collar_design_labels/ba453aef46c484d4f3... collar_design nnynn\n",
"2 Images/coat_length_labels/ac448ded8b3ebd88a998... coat_length nnnnnynn\n",
"3 Images/coat_length_labels/206b38af4f2d55393865... coat_length nnnnnynn\n",
"4 Images/pant_length_labels/7da34d442a8dcc7c84a7... pant_length nnynnn"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Counter({'coat_length': 11320,\n",
" 'collar_design': 8393,\n",
" 'lapel_design': 7034,\n",
" 'neck_design': 5696,\n",
" 'neckline_design': 17148,\n",
" 'pant_length': 7460,\n",
" 'skirt_length': 9223,\n",
" 'sleeve_length': 13299})"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('base/Annotations/label.csv', header=None)\n",
"df.columns = ['filename', 'label_name', 'label']\n",
"df = df.sample(frac=1).reset_index(drop=True) # shuffle\n",
"\n",
"df.label_name = df.label_name.str.replace('_labels', '')\n",
"\n",
"display(df.head())\n",
"c = Counter(df.label_name)\n",
"c"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:23:15.523937Z",
"start_time": "2018-04-07T17:23:15.474352Z"
},
"code_folding": []
},
"outputs": [
{
"data": {
"text/plain": [
"{'coat_length': 8,\n",
" 'collar_design': 5,\n",
" 'lapel_design': 5,\n",
" 'neck_design': 5,\n",
" 'neckline_design': 10,\n",
" 'pant_length': 6,\n",
" 'skirt_length': 6,\n",
" 'sleeve_length': 9}"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"label_count = dict([(x, len(df[df.label_name == x].label.values[0])) for x in c.keys()])\n",
"label_names = list(label_count.keys())\n",
"display(label_count)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 生成 y"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:23:17.137384Z",
"start_time": "2018-04-07T17:23:15.524971Z"
}
},
"outputs": [],
"source": [
"fnames = df['filename'].values\n",
"width = 331\n",
"n = len(df)\n",
"y = [np.zeros((n, label_count[x])) for x in label_count.keys()]\n",
"for i in range(n):\n",
" label_name = df.label_name[i]\n",
" label = df.label[i]\n",
" y[label_names.index(label_name)][i, label.find('y')] = 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 读取图片"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:24:15.086526Z",
"start_time": "2018-04-07T17:23:17.140308Z"
},
"code_folding": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 79573/79573 [00:57<00:00, 1377.73it/s]\n"
]
}
],
"source": [
"def f(index):\n",
" return index, cv2.resize(cv2.imread('base/'+fnames[index]), (width, width))\n",
"\n",
"X = np.zeros((n, width, width, 3), dtype=np.uint8)\n",
"with multiprocessing.Pool(12) as pool:\n",
" with tqdm(pool.imap_unordered(f, range(n)), total=n) as pbar:\n",
" for i, img in pbar:\n",
" X[i] = img[:,:,::-1]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:24:15.093182Z",
"start_time": "2018-04-07T17:24:15.088621Z"
}
},
"outputs": [],
"source": [
"n_train = int(n*0.8)\n",
"\n",
"X_train = X[:n_train]\n",
"X_valid = X[n_train:]\n",
"y_train = [x[:n_train] for x in y]\n",
"y_valid = [x[n_train:] for x in y]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:24:15.099505Z",
"start_time": "2018-04-07T17:24:15.094585Z"
}
},
"outputs": [],
"source": [
"def display_images(imgs, w=8, h=4, figsize=(24, 12)):\n",
" plt.figure(figsize=figsize)\n",
" for i in range(w*h):\n",
" plt.subplot(h, w, i+1)\n",
" plt.imshow(imgs[i])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:24:15.116015Z",
"start_time": "2018-04-07T17:24:15.100880Z"
}
},
"outputs": [],
"source": [
"class Generator():\n",
" def __init__(self, X, y, batch_size=32, aug=False):\n",
" def generator():\n",
" idg = ImageDataGenerator(horizontal_flip=True,\n",
" rotation_range=20,\n",
" zoom_range=0.2)\n",
" while True:\n",
" for i in range(0, len(X), batch_size):\n",
" X_batch = X[i:i+batch_size].copy()\n",
" y_barch = [x[i:i+batch_size] for x in y]\n",
" if aug:\n",
" for j in range(len(X_batch)):\n",
" X_batch[j] = idg.random_transform(X_batch[j])\n",
" yield X_batch, y_barch\n",
" self.generator = generator()\n",
" self.steps = len(X) // batch_size + 1"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:24:15.123929Z",
"start_time": "2018-04-07T17:24:15.117484Z"
}
},
"outputs": [],
"source": [
"gen_train = Generator(X_train, y_train, batch_size=32, aug=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 数据集探索\n",
"\n",
"## 类别分布"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:24:15.130162Z",
"start_time": "2018-04-07T17:24:15.125259Z"
}
},
"outputs": [],
"source": [
"# plt.figure(figsize=(26, 14))\n",
"# for i in range(8):\n",
"# plt.subplot(2, 4, i+1)\n",
"# counts = Counter(y[i].argmax(axis=-1)[np.where(y[i].any(axis=-1))])\n",
"# pd.Series(counts).plot('bar')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 抽样可视化"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:24:15.138224Z",
"start_time": "2018-04-07T17:24:15.131739Z"
}
},
"outputs": [],
"source": [
"# plt.figure(figsize=(26, 14))\n",
"# w = 8\n",
"# h = 4\n",
"# for i in range(w*h):\n",
"# plt.subplot(h, w, i+1)\n",
"# index = np.random.randint(n)\n",
"# plt.title(str([y[x][index].argmax() if y[x][index].any() else -1 for x in range(8) ]))\n",
"# plt.imshow(X[index])\n",
"# plt.axis('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 搭建模型并训练\n",
"\n",
"## 搭建模型"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:24:15.145368Z",
"start_time": "2018-04-07T17:24:15.139606Z"
}
},
"outputs": [],
"source": [
"def acc(y_true, y_pred):\n",
" index = tf.reduce_any(y_true > 0.5, axis=-1)\n",
" res = tf.equal(tf.argmax(y_true, axis=-1), tf.argmax(y_pred, axis=-1))\n",
" index = tf.cast(index, tf.float32)\n",
" res = tf.cast(res, tf.float32)\n",
" return tf.reduce_sum(res * index) / (tf.reduce_sum(index) + 1e-7)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:25:14.609365Z",
"start_time": "2018-04-07T17:24:15.146696Z"
}
},
"outputs": [
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],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base_model = NASNetLarge(weights='imagenet', input_shape=(width, width, 3), include_top=False, pooling='avg')\n",
"\n",
"input_tensor = Input((width, width, 3))\n",
"x = input_tensor\n",
"x = Lambda(nasnet.preprocess_input)(x)\n",
"x = base_model(x)\n",
"x = Dropout(0.5)(x)\n",
"x = [Dense(count, activation='softmax', name=name)(x) for name, count in label_count.items()]\n",
"\n",
"model = Model(input_tensor, x)\n",
"model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])\n",
"\n",
"from IPython.display import SVG\n",
"from keras.utils.vis_utils import model_to_dot, plot_model\n",
"\n",
"plot_model(model, show_shapes=True, to_file='model_simple.png')\n",
"SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:27:36.541858Z",
"start_time": "2018-04-07T17:25:14.610710Z"
}
},
"outputs": [],
"source": [
"model2 = multi_gpu_model(model, n_gpus)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T17:27:36.545662Z",
"start_time": "2018-04-07T17:27:36.543513Z"
}
},
"outputs": [],
"source": [
"# opt = SGD(1e-3, momentum=0.9, nesterov=True, decay=1e-5)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T19:26:22.717817Z",
"start_time": "2018-04-07T17:27:36.547216Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/2\n",
"1990/1990 [==============================] - 3637s 2s/step - loss: 0.9191 - skirt_length_loss: 0.0899 - collar_design_loss: 0.0950 - coat_length_loss: 0.1324 - pant_length_loss: 0.0665 - neckline_design_loss: 0.2276 - lapel_design_loss: 0.0754 - sleeve_length_loss: 0.1565 - neck_design_loss: 0.0758 - skirt_length_acc: 0.7035 - collar_design_acc: 0.6344 - coat_length_acc: 0.6599 - pant_length_acc: 0.6951 - neckline_design_acc: 0.6305 - lapel_design_acc: 0.6307 - sleeve_length_acc: 0.6449 - neck_design_acc: 0.5332 - val_loss: 0.6083 - val_skirt_length_loss: 0.0512 - val_collar_design_loss: 0.0676 - val_coat_length_loss: 0.0879 - val_pant_length_loss: 0.0449 - val_neckline_design_loss: 0.1584 - val_lapel_design_loss: 0.0478 - val_sleeve_length_loss: 0.0960 - val_neck_design_loss: 0.0545 - val_skirt_length_acc: 0.5910 - val_collar_design_acc: 0.5061 - val_coat_length_acc: 0.6075 - val_pant_length_acc: 0.5270 - val_neckline_design_acc: 0.6930 - val_lapel_design_acc: 0.4736 - val_sleeve_length_acc: 0.6544 - val_neck_design_acc: 0.3755\n",
"Epoch 2/2\n",
"1990/1990 [==============================] - 3440s 2s/step - loss: 0.5394 - skirt_length_loss: 0.0565 - collar_design_loss: 0.0582 - coat_length_loss: 0.0797 - pant_length_loss: 0.0391 - neckline_design_loss: 0.1297 - lapel_design_loss: 0.0422 - sleeve_length_loss: 0.0853 - neck_design_loss: 0.0489 - skirt_length_acc: 0.8123 - collar_design_acc: 0.7756 - coat_length_acc: 0.7939 - pant_length_acc: 0.8053 - neckline_design_acc: 0.7990 - lapel_design_acc: 0.7909 - sleeve_length_acc: 0.8148 - neck_design_acc: 0.6859 - val_loss: 0.5533 - val_skirt_length_loss: 0.0458 - val_collar_design_loss: 0.0624 - val_coat_length_loss: 0.0826 - val_pant_length_loss: 0.0409 - val_neckline_design_loss: 0.1371 - val_lapel_design_loss: 0.0490 - val_sleeve_length_loss: 0.0908 - val_neck_design_loss: 0.0448 - val_skirt_length_acc: 0.6060 - val_collar_design_acc: 0.5263 - val_coat_length_acc: 0.6107 - val_pant_length_acc: 0.5369 - val_neckline_design_acc: 0.7293 - val_lapel_design_acc: 0.4752 - val_sleeve_length_acc: 0.6794 - val_neck_design_acc: 0.3972\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f842eb77e48>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model2.compile(optimizer=Adam(1e-4), loss='categorical_crossentropy', metrics=[acc])\n",
"model2.fit_generator(gen_train.generator, steps_per_epoch=gen_train.steps, epochs=2, validation_data=(X_valid, y_valid))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T22:02:58.261416Z",
"start_time": "2018-04-07T19:26:22.719422Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/3\n",
"1990/1990 [==============================] - 3266s 2s/step - loss: 0.3240 - skirt_length_loss: 0.0344 - collar_design_loss: 0.0373 - coat_length_loss: 0.0482 - pant_length_loss: 0.0250 - neckline_design_loss: 0.0757 - lapel_design_loss: 0.0225 - sleeve_length_loss: 0.0507 - neck_design_loss: 0.0301 - skirt_length_acc: 0.8773 - collar_design_acc: 0.8431 - coat_length_acc: 0.8804 - pant_length_acc: 0.8643 - neckline_design_acc: 0.8781 - lapel_design_acc: 0.8650 - sleeve_length_acc: 0.8903 - neck_design_acc: 0.7746 - val_loss: 0.4108 - val_skirt_length_loss: 0.0391 - val_collar_design_loss: 0.0455 - val_coat_length_loss: 0.0607 - val_pant_length_loss: 0.0323 - val_neckline_design_loss: 0.1020 - val_lapel_design_loss: 0.0334 - val_sleeve_length_loss: 0.0597 - val_neck_design_loss: 0.0381 - val_skirt_length_acc: 0.8545 - val_collar_design_acc: 0.8131 - val_coat_length_acc: 0.8499 - val_pant_length_acc: 0.8483 - val_neckline_design_acc: 0.8577 - val_lapel_design_acc: 0.8234 - val_sleeve_length_acc: 0.8697 - val_neck_design_acc: 0.7461\n",
"Epoch 2/3\n",
"1990/1990 [==============================] - 3040s 2s/step - loss: 0.2606 - skirt_length_loss: 0.0275 - collar_design_loss: 0.0299 - coat_length_loss: 0.0387 - pant_length_loss: 0.0200 - neckline_design_loss: 0.0625 - lapel_design_loss: 0.0173 - sleeve_length_loss: 0.0393 - neck_design_loss: 0.0254 - skirt_length_acc: 0.8938 - collar_design_acc: 0.8746 - coat_length_acc: 0.9028 - pant_length_acc: 0.8820 - neckline_design_acc: 0.9028 - lapel_design_acc: 0.8885 - sleeve_length_acc: 0.9103 - neck_design_acc: 0.7941 - val_loss: 0.4006 - val_skirt_length_loss: 0.0386 - val_collar_design_loss: 0.0443 - val_coat_length_loss: 0.0593 - val_pant_length_loss: 0.0324 - val_neckline_design_loss: 0.0994 - val_lapel_design_loss: 0.0313 - val_sleeve_length_loss: 0.0576 - val_neck_design_loss: 0.0376 - val_skirt_length_acc: 0.8546 - val_collar_design_acc: 0.8135 - val_coat_length_acc: 0.8502 - val_pant_length_acc: 0.8590 - val_neckline_design_acc: 0.8616 - val_lapel_design_acc: 0.8357 - val_sleeve_length_acc: 0.8771 - val_neck_design_acc: 0.7506\n",
"Epoch 3/3\n",
"1990/1990 [==============================] - 3039s 2s/step - loss: 0.2268 - skirt_length_loss: 0.0244 - collar_design_loss: 0.0258 - coat_length_loss: 0.0337 - pant_length_loss: 0.0175 - neckline_design_loss: 0.0552 - lapel_design_loss: 0.0139 - sleeve_length_loss: 0.0338 - neck_design_loss: 0.0224 - skirt_length_acc: 0.9022 - collar_design_acc: 0.8864 - coat_length_acc: 0.9156 - pant_length_acc: 0.8942 - neckline_design_acc: 0.9120 - lapel_design_acc: 0.8995 - sleeve_length_acc: 0.9294 - neck_design_acc: 0.8086 - val_loss: 0.3984 - val_skirt_length_loss: 0.0380 - val_collar_design_loss: 0.0448 - val_coat_length_loss: 0.0584 - val_pant_length_loss: 0.0310 - val_neckline_design_loss: 0.0998 - val_lapel_design_loss: 0.0322 - val_sleeve_length_loss: 0.0573 - val_neck_design_loss: 0.0367 - val_skirt_length_acc: 0.8595 - val_collar_design_acc: 0.8226 - val_coat_length_acc: 0.8503 - val_pant_length_acc: 0.8603 - val_neckline_design_acc: 0.8641 - val_lapel_design_acc: 0.8359 - val_sleeve_length_acc: 0.8773 - val_neck_design_acc: 0.7527\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f83e3921b70>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model2.compile(optimizer=Adam(1e-5), loss='categorical_crossentropy', metrics=[acc])\n",
"model2.fit_generator(gen_train.generator, steps_per_epoch=gen_train.steps, epochs=3, validation_data=(X_valid, y_valid))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T22:58:21.988308Z",
"start_time": "2018-04-07T22:02:58.262769Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/1\n",
"1990/1990 [==============================] - 3275s 2s/step - loss: 0.1859 - skirt_length_loss: 0.0203 - collar_design_loss: 0.0215 - coat_length_loss: 0.0262 - pant_length_loss: 0.0149 - neckline_design_loss: 0.0456 - lapel_design_loss: 0.0118 - sleeve_length_loss: 0.0269 - neck_design_loss: 0.0186 - skirt_length_acc: 0.9141 - collar_design_acc: 0.9001 - coat_length_acc: 0.9367 - pant_length_acc: 0.9030 - neckline_design_acc: 0.9278 - lapel_design_acc: 0.9106 - sleeve_length_acc: 0.9429 - neck_design_acc: 0.8237 - val_loss: 0.3961 - val_skirt_length_loss: 0.0376 - val_collar_design_loss: 0.0453 - val_coat_length_loss: 0.0584 - val_pant_length_loss: 0.0313 - val_neckline_design_loss: 0.0993 - val_lapel_design_loss: 0.0315 - val_sleeve_length_loss: 0.0570 - val_neck_design_loss: 0.0357 - val_skirt_length_acc: 0.8550 - val_collar_design_acc: 0.8224 - val_coat_length_acc: 0.8563 - val_pant_length_acc: 0.8618 - val_neckline_design_acc: 0.8626 - val_lapel_design_acc: 0.8400 - val_sleeve_length_acc: 0.8751 - val_neck_design_acc: 0.7569\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f83179491d0>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model2.compile(optimizer=Adam(1e-6), loss='categorical_crossentropy', metrics=[acc])\n",
"model2.fit_generator(gen_train.generator, steps_per_epoch=gen_train.steps, epochs=1, validation_data=(X_valid, y_valid))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 计算验证集准确率"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T22:58:53.275440Z",
"start_time": "2018-04-07T22:58:21.990355Z"
}
},
"outputs": [],
"source": [
"model.save('model_%s.h5' % model_name)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T23:01:37.022671Z",
"start_time": "2018-04-07T22:58:53.276890Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"15915/15915 [==============================] - 164s 10ms/step\n",
"skirt_length 0.8871057000553404\n",
"collar_design 0.8586102719033233\n",
"coat_length 0.8683015440508629\n",
"pant_length 0.89374185136897\n",
"neckline_design 0.8618533257532689\n",
"lapel_design 0.8917018284106891\n",
"sleeve_length 0.8756613756613757\n",
"neck_design 0.8346595932802829\n"
]
}
],
"source": [
"y_pred = model2.predict(X_valid, batch_size=128, verbose=1)\n",
"a = np.array([x.any(axis=-1) for x in y_valid]).T.astype('uint8')\n",
"b = [np.where((a == np.eye(8)[x]).all(axis=-1))[0] for x in range(8)]\n",
"for c in range(8):\n",
" y_pred2 = y_pred[c][b[c]].argmax(axis=-1)\n",
" y_true2 = y_valid[c][b[c]].argmax(axis=-1)\n",
" print(label_names[c], (y_pred2 == y_true2).mean())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T23:01:37.038867Z",
"start_time": "2018-04-07T23:01:37.025054Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Counter({'coat_length': 11320,\n",
" 'collar_design': 8393,\n",
" 'lapel_design': 7034,\n",
" 'neck_design': 5696,\n",
" 'neckline_design': 17148,\n",
" 'pant_length': 7460,\n",
" 'skirt_length': 9223,\n",
" 'sleeve_length': 13299})"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"counts = Counter(df.label_name)\n",
"counts"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T23:01:37.050973Z",
"start_time": "2018-04-07T23:01:37.040700Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8713447116844438\n"
]
}
],
"source": [
"s = 0\n",
"n = 0\n",
"for c in range(8):\n",
" y_pred2 = y_pred[c][b[c]].argmax(axis=-1)\n",
" y_true2 = y_valid[c][b[c]].argmax(axis=-1)\n",
" s += counts[label_names[c]] * (y_pred2 == y_true2).mean()\n",
" n += counts[label_names[c]]\n",
"print(s / n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 在测试集上预测"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T23:01:37.075633Z",
"start_time": "2018-04-07T23:01:37.052291Z"
}
},
"outputs": [
{
"data": {
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <td>collar_design_labels</td>\n",
" <td>?</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Images/collar_design_labels/677e1183282769a3fe...</td>\n",
" <td>collar_design_labels</td>\n",
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" <tr>\n",
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" <th>3</th>\n",
" <td>Images/collar_design_labels/1712bf3d7b57d779ad...</td>\n",
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" <td>?</td>\n",
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" <th>4</th>\n",
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"text/plain": [
" filename label_name \\\n",
"0 Images/collar_design_labels/60951f5761ea3a01d6... collar_design_labels \n",
"1 Images/collar_design_labels/677e1183282769a3fe... collar_design_labels \n",
"2 Images/collar_design_labels/3f5a2be205e5d253eb... collar_design_labels \n",
"3 Images/collar_design_labels/1712bf3d7b57d779ad... collar_design_labels \n",
"4 Images/collar_design_labels/0b2b4254f35ce3a41a... collar_design_labels \n",
"\n",
" label \n",
"0 ? \n",
"1 ? \n",
"2 ? \n",
"3 ? \n",
"4 ? "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test = pd.read_csv('rank/Tests/question.csv', header=None)\n",
"df_test.columns = ['filename', 'label_name', 'label']\n",
"\n",
"fnames_test = df_test.filename\n",
"\n",
"n_test = len(df_test)\n",
"df_test.head()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T23:01:53.661369Z",
"start_time": "2018-04-07T23:01:37.077016Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 10080/10080 [00:14<00:00, 682.05it/s]\n"
]
}
],
"source": [
"def f(index):\n",
" return index, cv2.resize(cv2.imread('rank/'+fnames_test[index]), (width, width))\n",
"\n",
"X_test = np.zeros((n_test, width, width, 3), dtype=np.uint8)\n",
"with multiprocessing.Pool(12) as pool:\n",
" with tqdm(pool.imap_unordered(f, range(n_test)), total=n_test) as pbar:\n",
" for i, img in pbar:\n",
" X_test[i] = img[:,:,::-1]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T23:03:12.008784Z",
"start_time": "2018-04-07T23:01:53.664178Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10080/10080 [==============================] - 78s 8ms/step\n"
]
}
],
"source": [
"y_pred = model2.predict(X_test, batch_size=128, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T23:03:13.281597Z",
"start_time": "2018-04-07T23:03:12.009929Z"
}
},
"outputs": [],
"source": [
"for i in range(n_test):\n",
" problem_name = df_test.label_name[i].replace('_labels', '')\n",
" problem_index = label_names.index(problem_name)\n",
" probs = y_pred[problem_index][i]\n",
" df_test.label[i] = ';'.join(np.char.mod('%.8f', probs))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T23:03:13.360156Z",
"start_time": "2018-04-07T23:03:13.282708Z"
}
},
"outputs": [],
"source": [
"fname_csv = 'pred_%s.csv' % model_name\n",
"fname_zip = 'pred_%s.zip' % model_name\n",
"df_test.to_csv(fname_csv, index=None, header=None)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-07T23:03:13.758745Z",
"start_time": "2018-04-07T23:03:13.362310Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" adding: pred_nasnet_fine_tuning_8clf_2.csv (deflated 69%)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"rm: 无法删除'pred_nasnet_fine_tuning_8clf_2.zip': 没有那个文件或目录\n"
]
}
],
"source": [
"%%bash -s $fname_csv $fname_zip\n",
"rm $2\n",
"zip $2 $1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
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