cleanup, more modes
Jeff Moe
parent
8dd443ab30
commit
8aee0ac624
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@ -74,29 +74,12 @@
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"from sklearn.decomposition import PCA\n",
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"\n",
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"# Seaborn pip dependency\n",
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"import seaborn as sns"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Interact\n",
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"# https://ipywidgets.readthedocs.io/en/stable/examples/Using%20Interact.html\n",
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"import seaborn as sns\n",
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"\n",
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"from __future__ import print_function\n",
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"from ipywidgets import interact, interactive, fixed, interact_manual\n",
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"import ipywidgets as widgets"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Display Images\n",
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"import ipywidgets as widgets\n",
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"\n",
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"from IPython.display import display, Image"
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]
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},
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@ -106,7 +89,13 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"ENCODING='FSK9k6'"
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"#ENCODING='APT'\n",
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"#ENCODING='CW'\n",
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"#ENCODING='FM'\n",
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"#ENCODING='FSK9k6'\n",
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"ENCODING='GMSK2k4'\n",
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"#ENCODING='GMSK4k8'\n",
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"#ENCODING='USB'"
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]
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},
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{
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@ -208,7 +197,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"print(num_test)"
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"print(\"Number of observations to test:\", num_test)"
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]
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},
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{
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@ -217,7 +206,6 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# This function will plot images in the form of a grid with 1 row and 3 columns where images are placed in each column.\n",
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"def plotImages(images_arr):\n",
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" fig, axes = plt.subplots(1, 3, figsize=(20,20))\n",
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" axes = axes.flatten()\n",
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@ -301,6 +289,13 @@
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" rating = 'good'\n",
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"print('Observation: %s' % (rating))"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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@ -69,18 +69,13 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Visualization\n",
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.decomposition import PCA\n",
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"# Seaborn pip dependency\n",
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"import seaborn as sns\n",
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"# Interact\n",
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"# https://ipywidgets.readthedocs.io/en/stable/examples/Using%20Interact.html\n",
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"from ipywidgets import interact, interactive, fixed, interact_manual\n",
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"import ipywidgets as widgets\n",
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"# Display Images\n",
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"from IPython.display import display, Image\n",
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"from IPython.display import SVG"
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]
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@ -91,13 +86,35 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"ENCODING='FSK9k6'\n",
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"#batch_size = 64\n",
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"#ENCODING='APT'\n",
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"#ENCODING='BPSK1k2' # Fail\n",
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"#ENCODING='FSK9k6'\n",
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"#ENCODING='FM'\n",
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"ENCODING='GMSK2k4'\n",
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"#ENCODING='GMSK4k8'\n",
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"#ENCODING='USB'"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#batch_size = 8\n",
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"#atch_size = 16\n",
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"#atch_size = 32\n",
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"batch_size = 64\n",
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"#batch_size = 128\n",
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"#batch_size = 256\n",
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"#epochs = 4\n",
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"batch_size = 128\n",
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"epochs = 4\n",
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"epochs = 8\n",
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"#IMG_WIDTH = 208\n",
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"#IMG_HEIGHT = 402\n",
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"IMG_WIDTH = 416\n",
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"IMG_HEIGHT = 803"
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"IMG_HEIGHT = 803\n",
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"#IMG_WIDTH = 823\n",
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"#IMG_HEIGHT = 1603"
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]
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},
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{
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@ -133,9 +150,9 @@
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"print('Validation bad images: ', num_val_bad)\n",
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"print('Validation images: ', total_val)\n",
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"print('')\n",
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"print('Reduce training and validation set')\n",
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"total_train = 5000\n",
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"total_val = 5000\n",
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"#print('Reduce training and validation set')\n",
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"#total_train = 1000\n",
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"#total_val = 1000\n",
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"print('Training reduced to: ', total_train)\n",
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"print('Validation reduced to: ', total_val)"
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]
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@ -390,7 +407,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"loss = history.history['loss']\n",
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@ -398,8 +417,10 @@
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"\n",
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"epochs_range = range(epochs)\n",
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"\n",
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"save_plot_dir = os.path.join('/srv/satnogs/data/models/', ENCODING)\n",
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"os.makedirs(save_plot_dir, exist_ok=True)\n",
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"plot_file=(\"wut-plot-\" + ENCODING + \".png\")\n",
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"save_path_plot = os.path.join('/srv/satnogs/data/models/', ENCODING, plot_file)\n",
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"save_path_plot = os.path.join(save_plot_dir, plot_file)\n",
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"print(save_path_plot)\n",
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"\n",
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"plt.figure(figsize=(8, 8))\n",
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@ -491,6 +512,13 @@
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"source": [
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"#SVG(model_to_dot(model).create(prog='dot', format='svg'))"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -13,7 +13,7 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -22,7 +22,7 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -31,7 +31,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -47,24 +47,16 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Start\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"print(\"Start\")"
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -73,7 +65,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -82,7 +74,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -91,7 +83,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -116,7 +108,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -129,7 +121,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -139,7 +131,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -152,7 +144,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -162,24 +154,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Python import done\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"print(\"Python import done\")"
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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": 19,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -190,7 +174,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -200,7 +184,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -210,7 +194,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -220,7 +204,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -230,7 +214,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -249,20 +233,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"total training good images: 16739\n",
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"total training bad images: 3961\n",
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"--\n",
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"Total training images: 20700\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"print('total training good images:', num_train_good)\n",
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"print('total training bad images:', num_train_bad)\n",
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"total validation good images: 16716\n",
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"total validation bad images: 3934\n",
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"--\n",
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"Total validation images: 20650\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"print('total validation good images:', num_val_good)\n",
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"print('total validation bad images:', num_val_bad)\n",
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