Dataset Viewer (First 5GB)
Search is not available for this dataset
text
stringlengths 2.46k
96.2M
| id
stringlengths 23
24
| file_path
stringclasses 46
values |
|---|---|---|
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2020-08-06T10:40:56.233652Z",
"iopub.status.busy": "2020-08-06T10:40:56.232704Z",
"iopub.status.idle": "2020-08-06T10:41:05.522338Z",
"shell.execute_reply": "2020-08-06T10:41:05.521627Z"
},
"papermill": {
"duration": 9.311589,
"end_time": "2020-08-06T10:41:05.522494",
"exception": false,
"start_time": "2020-08-06T10:40:56.210905",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"import numpy as np\n",
"import pickle\n",
"import cv2\n",
"from os import listdir\n",
"from sklearn.preprocessing import LabelBinarizer\n",
"from keras.models import Sequential\n",
"from keras.layers.normalization import BatchNormalization\n",
"from keras.layers.convolutional import Conv2D\n",
"from keras.layers.convolutional import MaxPooling2D\n",
"from keras.layers.core import Activation, Flatten, Dropout, Dense\n",
"from keras import backend as K\n",
"from keras.preprocessing.image import ImageDataGenerator\n",
"from keras.optimizers import Adam\n",
"from keras.preprocessing import image\n",
"from keras.preprocessing.image import img_to_array\n",
"from sklearn.preprocessing import MultiLabelBinarizer\n",
"from sklearn.model_selection import train_test_split\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import os\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2020-08-06T10:41:05.548114Z",
"iopub.status.busy": "2020-08-06T10:41:05.547085Z",
"iopub.status.idle": "2020-08-06T10:41:05.550404Z",
"shell.execute_reply": "2020-08-06T10:41:05.549637Z"
},
"papermill": {
"duration": 0.018841,
"end_time": "2020-08-06T10:41:05.550538",
"exception": false,
"start_time": "2020-08-06T10:41:05.531697",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"\n",
"\n",
"EPOCHS = 20\n",
"INIT_LR = 1e-5\n",
"BS = 8\n",
"default_image_size = tuple((256, 256))\n",
"image_size = 0\n",
"\n",
"width=256\n",
"height=256\n",
"depth=3"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2020-08-06T10:41:05.572377Z",
"iopub.status.busy": "2020-08-06T10:41:05.571267Z",
"iopub.status.idle": "2020-08-06T10:41:05.574854Z",
"shell.execute_reply": "2020-08-06T10:41:05.574181Z"
},
"papermill": {
"duration": 0.016512,
"end_time": "2020-08-06T10:41:05.574983",
"exception": false,
"start_time": "2020-08-06T10:41:05.558471",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#since number of classes being used is 3\n",
"n_classes = 3"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
"execution": {
"iopub.execute_input": "2020-08-06T10:41:05.611402Z",
"iopub.status.busy": "2020-08-06T10:41:05.610259Z",
"iopub.status.idle": "2020-08-06T10:41:05.613954Z",
"shell.execute_reply": "2020-08-06T10:41:05.613289Z"
},
"papermill": {
"duration": 0.0306,
"end_time": "2020-08-06T10:41:05.614127",
"exception": false,
"start_time": "2020-08-06T10:41:05.583527",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from keras import layers\n",
"from keras.models import Model\n",
"\n",
"optss = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)\n",
"def alexnet(in_shape=(256,256,3), n_classes=n_classes, opt=optss):\n",
" in_layer = layers.Input(in_shape)\n",
" conv1 = layers.Conv2D(96, 11, strides=4, activation='relu')(in_layer)\n",
" pool1 = layers.MaxPool2D(3, 2)(conv1)\n",
" conv2 = layers.Conv2D(256, 5, strides=1, padding='same', activation='relu')(pool1)\n",
" pool2 = layers.MaxPool2D(3, 2)(conv2)\n",
" conv3 = layers.Conv2D(384, 3, strides=1, padding='same', activation='relu')(pool2)\n",
" conv4 = layers.Conv2D(256, 3, strides=1, padding='same', activation='relu')(conv3)\n",
" pool3 = layers.MaxPool2D(3, 2)(conv4)\n",
" flattened = layers.Flatten()(pool3)\n",
" dense1 = layers.Dense(4096, activation='relu')(flattened)\n",
" drop1 = layers.Dropout(0.8)(dense1)\n",
" dense2 = layers.Dense(4096, activation='relu')(drop1)\n",
" drop2 = layers.Dropout(0.8)(dense2)\n",
" preds = layers.Dense(n_classes, activation='softmax')(drop2)\n",
"\n",
" model = Model(in_layer, preds)\n",
" model.compile(loss=\"categorical_crossentropy\", optimizer=opt,metrics=[\"accuracy\"])\n",
" return model\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2020-08-06T10:41:05.637852Z",
"iopub.status.busy": "2020-08-06T10:41:05.637006Z",
"iopub.status.idle": "2020-08-06T10:41:06.421195Z",
"shell.execute_reply": "2020-08-06T10:41:06.420331Z"
},
"papermill": {
"duration": 0.798492,
"end_time": "2020-08-06T10:41:06.421346",
"exception": false,
"start_time": "2020-08-06T10:41:05.622854",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"model = alexnet()"
]
},
{
"cell_type": "markdown",
"execution_count": null,
"metadata": {
"papermill": {
"duration": 0.008012,
"end_time": "2020-08-06T10:41:06.439999",
"exception": false,
"start_time": "2020-08-06T10:41:06.431987",
"status": "completed"
},
"tags": []
},
"source": [
"# Model Visualization"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2020-08-06T10:41:06.463535Z",
"iopub.status.busy": "2020-08-06T10:41:06.462673Z",
"iopub.status.idle": "2020-08-06T10:41:07.220259Z",
"shell.execute_reply": "2020-08-06T10:41:07.219574Z"
},
"papermill": {
"duration": 0.772176,
"end_time": "2020-08-06T10:41:07.220412",
"exception": false,
"start_time": "2020-08-06T10:41:06.448236",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from tensorflow.keras.utils import plot_model\n",
"plot_model(model, to_file='model.png')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2020-08-06T10:41:07.247513Z",
"iopub.status.busy": "2020-08-06T10:41:07.246667Z",
"iopub.status.idle": "2020-08-06T10:41:07.253855Z",
"shell.execute_reply": "2020-08-06T10:41:07.253129Z"
},
"papermill": {
"duration": 0.024093,
"end_time": "2020-08-06T10:41:07.253983",
"exception": false,
"start_time": "2020-08-06T10:41:07.229890",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"model\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_1 (InputLayer) [(None, 256, 256, 3)] 0 \n",
"_________________________________________________________________\n",
"conv2d (Conv2D) (None, 62, 62, 96) 34944 \n",
"_________________________________________________________________\n",
"max_pooling2d (MaxPooling2D) (None, 30, 30, 96) 0 \n",
"_________________________________________________________________\n",
"conv2d_1 (Conv2D) (None, 30, 30, 256) 614656 \n",
"_________________________________________________________________\n",
"max_pooling2d_1 (MaxPooling2 (None, 14, 14, 256) 0 \n",
"_________________________________________________________________\n",
"conv2d_2 (Conv2D) (None, 14, 14, 384) 885120 \n",
"_________________________________________________________________\n",
"conv2d_3 (Conv2D) (None, 14, 14, 256) 884992 \n",
"_________________________________________________________________\n",
"max_pooling2d_2 (MaxPooling2 (None, 6, 6, 256) 0 \n",
"_________________________________________________________________\n",
"flatten (Flatten) (None, 9216) 0 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 4096) 37752832 \n",
"_________________________________________________________________\n",
"dropout (Dropout) (None, 4096) 0 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 4096) 16781312 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 4096) 0 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 3) 12291 \n",
"=================================================================\n",
"Total params: 56,966,147\n",
"Trainable params: 56,966,147\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
}
],
"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": "3.7.6"
},
"papermill": {
"duration": 16.369791,
"end_time": "2020-08-06T10:41:07.373011",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2020-08-06T10:40:51.003220",
"version": "2.1.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
0040/259/40259107.ipynb
|
s3://data-agents/kaggle-outputs/sharded/009_00040.jsonl.gz
|
"{\"cells\":[{\"metadata\":{},\"cell_type\":\"markdown\",\"source\":\"Hi All, in this notebook I'll (...TRUNCATED) |
0040/259/40259553.ipynb
|
s3://data-agents/kaggle-outputs/sharded/009_00040.jsonl.gz
|
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) |
0040/259/40259600.ipynb
|
s3://data-agents/kaggle-outputs/sharded/009_00040.jsonl.gz
|
"{\"cells\":[{\"metadata\":{},\"cell_type\":\"markdown\",\"source\":\"<h1 align='center'>Welcome to (...TRUNCATED) |
0040/259/40259715.ipynb
|
s3://data-agents/kaggle-outputs/sharded/009_00040.jsonl.gz
|
"{\"cells\":[{\"metadata\":{\"_uuid\":\"8f2839f25d086af736a60e9eeb907d3b93b6e0e5\",\"_cell_guid\":\"(...TRUNCATED) |
0040/259/40259749.ipynb
|
s3://data-agents/kaggle-outputs/sharded/009_00040.jsonl.gz
|
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"execution_count\": null,\n \"metadat(...TRUNCATED) |
0040/259/40259815.ipynb
|
s3://data-agents/kaggle-outputs/sharded/009_00040.jsonl.gz
|
"{\"cells\":[{\"metadata\":{\"_uuid\":\"8f2839f25d086af736a60e9eeb907d3b93b6e0e5\",\"_cell_guid\":\"(...TRUNCATED) |
0040/259/40259824.ipynb
|
s3://data-agents/kaggle-outputs/sharded/009_00040.jsonl.gz
|
"{\n \"cells\": [\n {\n \"attachments\": {\n \"image.png\": {\n \"image/png\": \"iVBORw0KG(...TRUNCATED) |
0040/259/40259936.ipynb
|
s3://data-agents/kaggle-outputs/sharded/009_00040.jsonl.gz
|
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"execution_count\": null,\n \"metadat(...TRUNCATED) |
0040/260/40260363.ipynb
|
s3://data-agents/kaggle-outputs/sharded/009_00040.jsonl.gz
|
"{\"cells\":[{\"metadata\":{\"_uuid\":\"8f2839f25d086af736a60e9eeb907d3b93b6e0e5\",\"_cell_guid\":\"(...TRUNCATED) |
0040/261/40261055.ipynb
|
s3://data-agents/kaggle-outputs/sharded/009_00040.jsonl.gz
|
End of preview. Expand
in Data Studio
README.md exists but content is empty.
- Downloads last month
- 6