diff --git a/14_deep_computer_vision_with_cnns.ipynb b/14_deep_computer_vision_with_cnns.ipynb
index e1ef44d..873c66c 100644
--- a/14_deep_computer_vision_with_cnns.ipynb
+++ b/14_deep_computer_vision_with_cnns.ipynb
@@ -14,6 +14,17 @@
"_This notebook contains all the sample code in chapter 14._"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -25,7 +36,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "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."
+ "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."
]
},
{
@@ -42,11 +53,23 @@
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
- "# TensorFlow ≥2.0-preview is required\n",
+ "try:\n",
+ " # %tensorflow_version only exists in Colab.\n",
+ " %tensorflow_version 2.x\n",
+ " IS_COLAB = True\n",
+ "except Exception:\n",
+ " IS_COLAB = False\n",
+ "\n",
+ "# TensorFlow ≥2.0 is required\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"assert tf.__version__ >= \"2.0\"\n",
"\n",
+ "if not tf.test.is_gpu_available():\n",
+ " print(\"No GPU was detected. CNNs can be very slow without a GPU.\")\n",
+ " if IS_COLAB:\n",
+ " print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
+ "\n",
"# Common imports\n",
"import numpy as np\n",
"import os\n",
@@ -1412,7 +1435,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.8"
+ "version": "3.7.3"
},
"nav_menu": {},
"toc": {
diff --git a/15_processing_sequences_using_rnns_and_cnns.ipynb b/15_processing_sequences_using_rnns_and_cnns.ipynb
index 36b037b..f0f5f55 100644
--- a/15_processing_sequences_using_rnns_and_cnns.ipynb
+++ b/15_processing_sequences_using_rnns_and_cnns.ipynb
@@ -14,6 +14,17 @@
"_This notebook contains all the sample code in chapter 15._"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -25,7 +36,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "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."
+ "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."
]
},
{
@@ -42,11 +53,23 @@
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
- "# TensorFlow ≥2.0-preview is required\n",
+ "try:\n",
+ " # %tensorflow_version only exists in Colab.\n",
+ " %tensorflow_version 2.x\n",
+ " IS_COLAB = True\n",
+ "except Exception:\n",
+ " IS_COLAB = False\n",
+ "\n",
+ "# TensorFlow ≥2.0 is required\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"assert tf.__version__ >= \"2.0\"\n",
"\n",
+ "if not tf.test.is_gpu_available():\n",
+ " print(\"No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\")\n",
+ " if IS_COLAB:\n",
+ " print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
+ "\n",
"# Common imports\n",
"import numpy as np\n",
"import os\n",
@@ -1116,8 +1139,6 @@
"metadata": {},
"outputs": [],
"source": [
- "from tensorflow import keras\n",
- "\n",
"class GatedActivationUnit(keras.layers.Layer):\n",
" def __init__(self, activation=\"tanh\", **kwargs):\n",
" super().__init__(**kwargs)\n",
@@ -1367,7 +1388,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.8"
+ "version": "3.7.3"
},
"nav_menu": {},
"toc": {
diff --git a/16_nlp_with_rnns_and_attention.ipynb b/16_nlp_with_rnns_and_attention.ipynb
index e12c6b6..24db9a7 100644
--- a/16_nlp_with_rnns_and_attention.ipynb
+++ b/16_nlp_with_rnns_and_attention.ipynb
@@ -14,6 +14,17 @@
"_This notebook contains all the sample code in chapter 16._"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -25,7 +36,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "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."
+ "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."
]
},
{
@@ -42,11 +53,24 @@
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
- "# TensorFlow ≥2.0-preview is required\n",
+ "try:\n",
+ " # %tensorflow_version only exists in Colab.\n",
+ " %tensorflow_version 2.x\n",
+ " !pip install -q -U tensorflow-addons\n",
+ " IS_COLAB = True\n",
+ "except Exception:\n",
+ " IS_COLAB = False\n",
+ "\n",
+ "# TensorFlow ≥2.0 is required\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"assert tf.__version__ >= \"2.0\"\n",
"\n",
+ "if not tf.test.is_gpu_available():\n",
+ " print(\"No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\")\n",
+ " if IS_COLAB:\n",
+ " print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
+ "\n",
"# Common imports\n",
"import numpy as np\n",
"import os\n",
@@ -1213,7 +1237,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.8"
+ "version": "3.7.3"
},
"nav_menu": {},
"toc": {
diff --git a/17_autoencoders_and_gans.ipynb b/17_autoencoders_and_gans.ipynb
index 16b2151..fa989a8 100644
--- a/17_autoencoders_and_gans.ipynb
+++ b/17_autoencoders_and_gans.ipynb
@@ -14,6 +14,17 @@
"_This notebook contains all the sample code in chapter 17._"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -25,7 +36,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "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."
+ "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."
]
},
{
@@ -42,11 +53,23 @@
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
- "# TensorFlow ≥2.0-preview is required\n",
+ "try:\n",
+ " # %tensorflow_version only exists in Colab.\n",
+ " %tensorflow_version 2.x\n",
+ " IS_COLAB = True\n",
+ "except Exception:\n",
+ " IS_COLAB = False\n",
+ "\n",
+ "# TensorFlow ≥2.0 is required\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"assert tf.__version__ >= \"2.0\"\n",
"\n",
+ "if not tf.test.is_gpu_available():\n",
+ " print(\"No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\")\n",
+ " if IS_COLAB:\n",
+ " print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
+ "\n",
"# Common imports\n",
"import numpy as np\n",
"import os\n",
@@ -1598,7 +1621,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.8"
+ "version": "3.7.3"
},
"nav_menu": {
"height": "381px",
diff --git a/18_reinforcement_learning.ipynb b/18_reinforcement_learning.ipynb
index fde8b2b..55458ae 100644
--- a/18_reinforcement_learning.ipynb
+++ b/18_reinforcement_learning.ipynb
@@ -14,6 +14,17 @@
"_This notebook contains all the sample code in chapter 18_."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -25,7 +36,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "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."
+ "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."
]
},
{
@@ -42,11 +53,25 @@
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
- "# TensorFlow ≥2.0-preview is required\n",
+ "try:\n",
+ " # %tensorflow_version only exists in Colab.\n",
+ " %tensorflow_version 2.x\n",
+ " !apt update && apt install -y libpq-dev libsdl2-dev swig xorg-dev xvfb\n",
+ " !pip install -q -U tf-agents-nightly pyvirtualdisplay gym[atari]\n",
+ " IS_COLAB = True\n",
+ "except Exception:\n",
+ " IS_COLAB = False\n",
+ "\n",
+ "# TensorFlow ≥2.0 is required\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"assert tf.__version__ >= \"2.0\"\n",
"\n",
+ "if not tf.test.is_gpu_available():\n",
+ " print(\"No GPU was detected. CNNs can be very slow without a GPU.\")\n",
+ " if IS_COLAB:\n",
+ " print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
+ "\n",
"# Common imports\n",
"import numpy as np\n",
"import os\n",
@@ -2752,7 +2777,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.4"
+ "version": "3.7.3"
}
},
"nbformat": 4,
diff --git a/19_training_and_deploying_at_scale.ipynb b/19_training_and_deploying_at_scale.ipynb
index 2e0e093..6c0a9a3 100644
--- a/19_training_and_deploying_at_scale.ipynb
+++ b/19_training_and_deploying_at_scale.ipynb
@@ -14,12 +14,23 @@
"_This notebook contains all the sample code in chapter 19._"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup\n",
- "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"
+ "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"
]
},
{
@@ -36,11 +47,27 @@
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
- "# TensorFlow ≥2.0-preview is required\n",
+ "try:\n",
+ " # %tensorflow_version only exists in Colab.\n",
+ " %tensorflow_version 2.x\n",
+ " !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",
+ " !curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | apt-key add -\n",
+ " !apt update && apt-get install -y tensorflow-model-server\n",
+ " !pip install -q -U tensorflow-serving-api\n",
+ " IS_COLAB = True\n",
+ "except Exception:\n",
+ " IS_COLAB = False\n",
+ "\n",
+ "# TensorFlow ≥2.0 is required\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"assert tf.__version__ >= \"2.0\"\n",
"\n",
+ "if not tf.test.is_gpu_available():\n",
+ " print(\"No GPU was detected. CNNs can be very slow without a GPU.\")\n",
+ " if IS_COLAB:\n",
+ " print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
+ "\n",
"# Common imports\n",
"import numpy as np\n",
"import os\n",
@@ -292,11 +319,49 @@
"Once you are finished using it, press Ctrl-C to shut down the server."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "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:"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"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,