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7 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 8e4927d3ea | |||
| fb2e70e1da | |||
| abb01e949e | |||
| 2aca23985e | |||
| 2197ddff33 | |||
| bcb3028e9a | |||
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b2c0792f20 |
6
.ipynb_checkpoints/Untitled-checkpoint.ipynb
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6
.ipynb_checkpoints/Untitled-checkpoint.ipynb
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@ -0,0 +1,6 @@
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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6
.ipynb_checkpoints/Untitled1-checkpoint.ipynb
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6
.ipynb_checkpoints/Untitled1-checkpoint.ipynb
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@ -0,0 +1,6 @@
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{
|
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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1915
Untitled.ipynb
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1915
Untitled.ipynb
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Load Diff
149134
dev-0/out.tsv
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149134
dev-0/out.tsv
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File diff suppressed because it is too large
Load Diff
170
keras_class.ipynb
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170
keras_class.ipynb
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@ -0,0 +1,170 @@
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{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
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"execution_count": 1,
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"id": "9e3e0aed",
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"metadata": {},
|
||||
"outputs": [],
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"source": [
|
||||
"from keras.preprocessing.image import ImageDataGenerator\n",
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"from keras.models import Sequential\n",
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"from keras.layers import Conv2D, MaxPooling2D\n",
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"from keras.layers import Activation, Dropout, Flatten, Dense\n",
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"from keras import backend as K\n",
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" \n",
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"img_width, img_height = 224, 224"
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]
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},
|
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{
|
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"cell_type": "code",
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"execution_count": 2,
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"id": "976c0dc7",
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"metadata": {},
|
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"outputs": [],
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"source": [
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"train_data_dir = 'v_data/train'\n",
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"validation_data_dir = 'v_data/test'\n",
|
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"nb_train_samples =400\n",
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"nb_validation_samples = 100\n",
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"epochs = 10\n",
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"batch_size = 16"
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]
|
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},
|
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "9bf78481",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"# check format\n",
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"\n",
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"\n",
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"if K.image_data_format() == 'channels_first':\n",
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" input_shape = (3, img_width, img_height)\n",
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"else:\n",
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" input_shape = (img_width, img_height, 3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "a0663374",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"model = Sequential()\n",
|
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"model.add(Conv2D(32, (2, 2), input_shape=input_shape))\n",
|
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"model.add(Activation('relu'))\n",
|
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"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
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" \n",
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"model.add(Conv2D(32, (2, 2)))\n",
|
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"model.add(Activation('relu'))\n",
|
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"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
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" \n",
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"model.add(Conv2D(64, (2, 2)))\n",
|
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"model.add(Activation('relu'))\n",
|
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"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
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" \n",
|
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"model.add(Flatten())\n",
|
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"model.add(Dense(64))\n",
|
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"model.add(Activation('relu'))\n",
|
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"model.add(Dropout(0.5))\n",
|
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"model.add(Dense(1))\n",
|
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"model.add(Activation('sigmoid'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "6fadd7e5",
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"metadata": {},
|
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"outputs": [],
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||||
"source": [
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"\n",
|
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"model.compile(loss='binary_crossentropy',\n",
|
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" optimizer='rmsprop',\n",
|
||||
" metrics=['accuracy'])"
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
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"id": "0bb1a7ba",
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"metadata": {},
|
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"outputs": [
|
||||
{
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||||
"ename": "FileNotFoundError",
|
||||
"evalue": "[WinError 3] The system cannot find the path specified: 'v_data/train'",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
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"Input \u001b[1;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 9>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m train_datagen \u001b[38;5;241m=\u001b[39m ImageDataGenerator(\n\u001b[0;32m 2\u001b[0m rescale\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m255\u001b[39m,\n\u001b[0;32m 3\u001b[0m shear_range\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m,\n\u001b[0;32m 4\u001b[0m zoom_range\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m,\n\u001b[0;32m 5\u001b[0m horizontal_flip\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 7\u001b[0m test_datagen \u001b[38;5;241m=\u001b[39m ImageDataGenerator(rescale\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m255\u001b[39m)\n\u001b[1;32m----> 9\u001b[0m train_generator \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_datagen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflow_from_directory\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_data_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mimg_width\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimg_height\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43mclass_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbinary\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 15\u001b[0m validation_generator \u001b[38;5;241m=\u001b[39m test_datagen\u001b[38;5;241m.\u001b[39mflow_from_directory(\n\u001b[0;32m 16\u001b[0m validation_data_dir,\n\u001b[0;32m 17\u001b[0m target_size\u001b[38;5;241m=\u001b[39m(img_width, img_height),\n\u001b[0;32m 18\u001b[0m batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[0;32m 19\u001b[0m class_mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 21\u001b[0m model\u001b[38;5;241m.\u001b[39mfit_generator(\n\u001b[0;32m 22\u001b[0m train_generator,\n\u001b[0;32m 23\u001b[0m steps_per_epoch\u001b[38;5;241m=\u001b[39mnb_train_samples \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m batch_size,\n\u001b[0;32m 24\u001b[0m epochs\u001b[38;5;241m=\u001b[39mepochs,\n\u001b[0;32m 25\u001b[0m validation_data\u001b[38;5;241m=\u001b[39mvalidation_generator,\n\u001b[0;32m 26\u001b[0m validation_steps\u001b[38;5;241m=\u001b[39mnb_validation_samples \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m batch_size)\n",
|
||||
"File \u001b[1;32m~\\.conda\\envs\\py\\lib\\site-packages\\keras\\preprocessing\\image.py:1469\u001b[0m, in \u001b[0;36mImageDataGenerator.flow_from_directory\u001b[1;34m(self, directory, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation, keep_aspect_ratio)\u001b[0m\n\u001b[0;32m 1386\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mflow_from_directory\u001b[39m(\u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 1387\u001b[0m directory,\n\u001b[0;32m 1388\u001b[0m target_size\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m256\u001b[39m, \u001b[38;5;241m256\u001b[39m),\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1400\u001b[0m interpolation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnearest\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 1401\u001b[0m keep_aspect_ratio\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m 1402\u001b[0m \u001b[38;5;124;03m\"\"\"Takes the path to a directory & generates batches of augmented data.\u001b[39;00m\n\u001b[0;32m 1403\u001b[0m \n\u001b[0;32m 1404\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1467\u001b[0m \u001b[38;5;124;03m and `y` is a numpy array of corresponding labels.\u001b[39;00m\n\u001b[0;32m 1468\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1469\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDirectoryIterator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1470\u001b[0m \u001b[43m \u001b[49m\u001b[43mdirectory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1471\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1472\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtarget_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1473\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolor_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolor_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1474\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeep_aspect_ratio\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_aspect_ratio\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1475\u001b[0m \u001b[43m \u001b[49m\u001b[43mclasses\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclasses\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1476\u001b[0m \u001b[43m \u001b[49m\u001b[43mclass_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1477\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1478\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1479\u001b[0m \u001b[43m \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshuffle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1480\u001b[0m \u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1481\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_to_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_to_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1482\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_prefix\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_prefix\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1483\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1484\u001b[0m \u001b[43m \u001b[49m\u001b[43mfollow_links\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_links\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1485\u001b[0m \u001b[43m \u001b[49m\u001b[43msubset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1486\u001b[0m \u001b[43m \u001b[49m\u001b[43minterpolation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1487\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32m~\\.conda\\envs\\py\\lib\\site-packages\\keras\\preprocessing\\image.py:507\u001b[0m, in \u001b[0;36mDirectoryIterator.__init__\u001b[1;34m(self, directory, image_data_generator, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, data_format, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation, keep_aspect_ratio, dtype)\u001b[0m\n\u001b[0;32m 505\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m classes:\n\u001b[0;32m 506\u001b[0m classes \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m--> 507\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m subdir \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlistdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdirectory\u001b[49m\u001b[43m)\u001b[49m):\n\u001b[0;32m 508\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(directory, subdir)):\n\u001b[0;32m 509\u001b[0m classes\u001b[38;5;241m.\u001b[39mappend(subdir)\n",
|
||||
"\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] The system cannot find the path specified: 'v_data/train'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"train_datagen = ImageDataGenerator(\n",
|
||||
" rescale=1. / 255,\n",
|
||||
" shear_range=0.2,\n",
|
||||
" zoom_range=0.2,\n",
|
||||
" horizontal_flip=True)\n",
|
||||
" \n",
|
||||
"test_datagen = ImageDataGenerator(rescale=1. / 255)\n",
|
||||
" \n",
|
||||
"train_generator = train_datagen.flow_from_directory(\n",
|
||||
" train_data_dir,\n",
|
||||
" target_size=(img_width, img_height),\n",
|
||||
" batch_size=batch_size,\n",
|
||||
" class_mode='binary')\n",
|
||||
" \n",
|
||||
"validation_generator = test_datagen.flow_from_directory(\n",
|
||||
" validation_data_dir,\n",
|
||||
" target_size=(img_width, img_height),\n",
|
||||
" batch_size=batch_size,\n",
|
||||
" class_mode='binary')\n",
|
||||
" \n",
|
||||
"model.fit_generator(\n",
|
||||
" train_generator,\n",
|
||||
" steps_per_epoch=nb_train_samples // batch_size,\n",
|
||||
" epochs=epochs,\n",
|
||||
" validation_data=validation_generator,\n",
|
||||
" validation_steps=nb_validation_samples // batch_size)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "623ec03f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "py",
|
||||
"language": "python",
|
||||
"name": "py"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
148308
test-A/out.tsv
Normal file
148308
test-A/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
79119
test-B/out.tsv
Normal file
79119
test-B/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
1186898
train/expected.tsv
Normal file
1186898
train/expected.tsv
Normal file
File diff suppressed because it is too large
Load Diff
Binary file not shown.
1186898
train/in.tsv
Normal file
1186898
train/in.tsv
Normal file
File diff suppressed because it is too large
Load Diff
BIN
train/in.tsv.xz
BIN
train/in.tsv.xz
Binary file not shown.
Loading…
Reference in New Issue
Block a user