Make notebooks 14 to 19 runnable in Colab without changes
Aurélien Geron
parent
56d8c08d3e
commit
88dccccd5f
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@ -14,6 +14,17 @@
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"_This notebook contains all the sample code in chapter 14._"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<table align=\"left\">\n",
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" <td>\n",
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" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/14_deep_computer_vision_with_cnns.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
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" </td>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -25,7 +36,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0-preview."
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"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0."
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]
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},
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{
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@ -42,11 +53,23 @@
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\"\n",
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"\n",
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"# TensorFlow ≥2.0-preview is required\n",
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"try:\n",
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" # %tensorflow_version only exists in Colab.\n",
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" %tensorflow_version 2.x\n",
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" IS_COLAB = True\n",
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"except Exception:\n",
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" IS_COLAB = False\n",
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"\n",
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"# TensorFlow ≥2.0 is required\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"assert tf.__version__ >= \"2.0\"\n",
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"\n",
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"if not tf.test.is_gpu_available():\n",
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" print(\"No GPU was detected. CNNs can be very slow without a GPU.\")\n",
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" if IS_COLAB:\n",
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" print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
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"\n",
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"# Common imports\n",
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"import numpy as np\n",
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"import os\n",
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@ -1412,7 +1435,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.8"
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"version": "3.7.3"
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},
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"nav_menu": {},
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"toc": {
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@ -14,6 +14,17 @@
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"_This notebook contains all the sample code in chapter 15._"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<table align=\"left\">\n",
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" <td>\n",
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" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/15_processing_sequences_using_rnns_and_cnns.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
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" </td>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -25,7 +36,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0-preview."
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"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0."
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]
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},
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{
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@ -42,11 +53,23 @@
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\"\n",
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"\n",
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"# TensorFlow ≥2.0-preview is required\n",
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"try:\n",
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" # %tensorflow_version only exists in Colab.\n",
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" %tensorflow_version 2.x\n",
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" IS_COLAB = True\n",
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"except Exception:\n",
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" IS_COLAB = False\n",
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"\n",
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"# TensorFlow ≥2.0 is required\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"assert tf.__version__ >= \"2.0\"\n",
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"\n",
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"if not tf.test.is_gpu_available():\n",
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" print(\"No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\")\n",
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" if IS_COLAB:\n",
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" print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
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"\n",
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"# Common imports\n",
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"import numpy as np\n",
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"import os\n",
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@ -1116,8 +1139,6 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from tensorflow import keras\n",
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"\n",
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"class GatedActivationUnit(keras.layers.Layer):\n",
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" def __init__(self, activation=\"tanh\", **kwargs):\n",
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" super().__init__(**kwargs)\n",
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@ -1367,7 +1388,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.8"
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"version": "3.7.3"
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},
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"nav_menu": {},
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"toc": {
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@ -14,6 +14,17 @@
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"_This notebook contains all the sample code in chapter 16._"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<table align=\"left\">\n",
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" <td>\n",
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" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/16_nlp_with_rnns_and_attention.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
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" </td>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -25,7 +36,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0-preview."
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"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0."
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]
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},
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{
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@ -42,11 +53,24 @@
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\"\n",
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"\n",
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"# TensorFlow ≥2.0-preview is required\n",
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"try:\n",
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" # %tensorflow_version only exists in Colab.\n",
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" %tensorflow_version 2.x\n",
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" !pip install -q -U tensorflow-addons\n",
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" IS_COLAB = True\n",
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"except Exception:\n",
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" IS_COLAB = False\n",
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"\n",
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"# TensorFlow ≥2.0 is required\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"assert tf.__version__ >= \"2.0\"\n",
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"\n",
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"if not tf.test.is_gpu_available():\n",
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" print(\"No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\")\n",
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" if IS_COLAB:\n",
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" print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
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"\n",
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"# Common imports\n",
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"import numpy as np\n",
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"import os\n",
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@ -1213,7 +1237,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.8"
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"version": "3.7.3"
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},
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"nav_menu": {},
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"toc": {
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@ -14,6 +14,17 @@
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"_This notebook contains all the sample code in chapter 17._"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<table align=\"left\">\n",
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" <td>\n",
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" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/17_autoencoders_and_gans.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
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" </td>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -25,7 +36,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0-preview."
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"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0."
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]
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},
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{
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@ -42,11 +53,23 @@
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\"\n",
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"\n",
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"# TensorFlow ≥2.0-preview is required\n",
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"try:\n",
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" # %tensorflow_version only exists in Colab.\n",
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" %tensorflow_version 2.x\n",
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" IS_COLAB = True\n",
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"except Exception:\n",
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" IS_COLAB = False\n",
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"\n",
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"# TensorFlow ≥2.0 is required\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"assert tf.__version__ >= \"2.0\"\n",
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"\n",
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"if not tf.test.is_gpu_available():\n",
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" print(\"No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\")\n",
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" if IS_COLAB:\n",
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" print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
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"\n",
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"# Common imports\n",
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"import numpy as np\n",
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"import os\n",
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@ -1598,7 +1621,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.8"
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"version": "3.7.3"
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},
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"nav_menu": {
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"height": "381px",
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@ -14,6 +14,17 @@
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"_This notebook contains all the sample code in chapter 18_."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<table align=\"left\">\n",
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" <td>\n",
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" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/18_reinforcement_learning.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
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" </td>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -25,7 +36,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0-preview."
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"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0."
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]
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},
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{
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@ -42,11 +53,25 @@
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\"\n",
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"\n",
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"# TensorFlow ≥2.0-preview is required\n",
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"try:\n",
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" # %tensorflow_version only exists in Colab.\n",
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" %tensorflow_version 2.x\n",
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" !apt update && apt install -y libpq-dev libsdl2-dev swig xorg-dev xvfb\n",
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" !pip install -q -U tf-agents-nightly pyvirtualdisplay gym[atari]\n",
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" IS_COLAB = True\n",
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"except Exception:\n",
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" IS_COLAB = False\n",
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"\n",
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"# TensorFlow ≥2.0 is required\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"assert tf.__version__ >= \"2.0\"\n",
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"\n",
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"if not tf.test.is_gpu_available():\n",
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" print(\"No GPU was detected. CNNs can be very slow without a GPU.\")\n",
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" if IS_COLAB:\n",
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" print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
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"\n",
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"# Common imports\n",
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"import numpy as np\n",
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"import os\n",
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@ -2752,7 +2777,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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@ -14,12 +14,23 @@
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"_This notebook contains all the sample code in chapter 19._"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<table align=\"left\">\n",
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" <td>\n",
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" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/19_training_and_deploying_at_scale.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
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" </td>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Setup\n",
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"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0-preview.\n"
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"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0.\n"
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]
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},
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{
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@ -36,11 +47,27 @@
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\"\n",
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"\n",
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"# TensorFlow ≥2.0-preview is required\n",
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"try:\n",
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" # %tensorflow_version only exists in Colab.\n",
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" %tensorflow_version 2.x\n",
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" !echo \"deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal\" > /etc/apt/sources.list.d/tensorflow-serving.list\n",
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" !curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | apt-key add -\n",
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" !apt update && apt-get install -y tensorflow-model-server\n",
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" !pip install -q -U tensorflow-serving-api\n",
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" IS_COLAB = True\n",
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"except Exception:\n",
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" IS_COLAB = False\n",
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"\n",
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"# TensorFlow ≥2.0 is required\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"assert tf.__version__ >= \"2.0\"\n",
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"\n",
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"if not tf.test.is_gpu_available():\n",
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" print(\"No GPU was detected. CNNs can be very slow without a GPU.\")\n",
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" if IS_COLAB:\n",
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" print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
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"\n",
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"# Common imports\n",
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"import numpy as np\n",
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"import os\n",
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@ -292,11 +319,49 @@
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"Once you are finished using it, press Ctrl-C to shut down the server."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Alternatively, if `tensorflow_model_server` is installed (e.g., if you are running this notebook in Colab), then the following 3 cells will start the server:"
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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": 17,
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"metadata": {},
|
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"outputs": [],
|
||||
"source": [
|
||||
"os.environ[\"MODEL_DIR\"] = os.path.split(os.path.abspath(model_path))[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%bash --bg\n",
|
||||
"nohup tensorflow_model_server \\\n",
|
||||
" --rest_api_port=8501 \\\n",
|
||||
" --model_name=my_mnist_model \\\n",
|
||||
" --model_base_path=\"${MODEL_DIR}\" >server.log 2>&1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!tail server.log"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"\n",
|
||||
|
|
@ -308,7 +373,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -324,7 +389,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -338,7 +403,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -347,7 +412,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -364,7 +429,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -379,7 +444,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -393,7 +458,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -409,7 +474,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": 28,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
|
|
@ -418,7 +483,7 @@
|
|||
"output_name = model.output_names[0]\n",
|
||||
"outputs_proto = response.outputs[output_name]\n",
|
||||
"y_proba = tf.make_ndarray(outputs_proto)\n",
|
||||
"y_proba.numpy().round(2)"
|
||||
"y_proba.round(2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -430,7 +495,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -450,7 +515,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"execution_count": 30,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
|
|
@ -460,7 +525,7 @@
|
|||
"tf.random.set_seed(42)\n",
|
||||
"\n",
|
||||
"model = keras.models.Sequential([\n",
|
||||
" keras.layers.Flatten(input_shape=[28, 28]),\n",
|
||||
" keras.layers.Flatten(input_shape=[28, 28, 1]),\n",
|
||||
" keras.layers.Dense(50, activation=\"relu\"),\n",
|
||||
" keras.layers.Dense(50, activation=\"relu\"),\n",
|
||||
" keras.layers.Dense(10, activation=\"softmax\")\n",
|
||||
|
|
@ -473,7 +538,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -485,7 +550,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -494,7 +559,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -514,7 +579,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -529,7 +594,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -538,7 +603,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -562,7 +627,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -571,7 +636,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -586,7 +651,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -602,7 +667,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -619,7 +684,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -628,7 +693,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -637,7 +702,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"execution_count": 43,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -646,7 +711,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"execution_count": 44,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -665,7 +730,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"execution_count": 45,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -676,7 +741,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 43,
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -699,7 +764,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"execution_count": 47,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -714,7 +779,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -747,7 +812,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"execution_count": 49,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -758,7 +823,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -774,7 +839,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -823,7 +888,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"execution_count": 52,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -867,7 +932,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -900,7 +965,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"execution_count": 54,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -921,7 +986,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 52,
|
||||
"execution_count": 55,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -940,7 +1005,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -950,7 +1015,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -966,7 +1031,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"execution_count": 58,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -994,7 +1059,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"execution_count": 59,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1043,7 +1108,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"execution_count": 60,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1057,7 +1122,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1081,7 +1146,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
"version": "3.7.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
Loading…
Reference in New Issue