add .gitignore

6 files changed, 2 insertions, 846 deletions

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..272a953
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+.virtual_documents
+notebooks/.ipynb_checkpoints
diff --git a/.virtual_documents/notebooks/IBM Watson Visual Recognition.ipynb b/.virtual_documents/notebooks/IBM Watson Visual Recognition.ipynb
deleted file mode 100644
index adeabf5..0000000
--- a/.virtual_documents/notebooks/IBM Watson Visual Recognition.ipynb
+++ /dev/null
@@ -1,75 +0,0 @@
-
-
-
-pip install --upgrade --user "ibm-watson>=4.5.0"
-
-
-apikey = "<your-apikey>"
-version = "2018-03-19"
-url = "<your-url>"
-
-
-import json
-from ibm_watson import VisualRecognitionV3
-from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
-
-authenticator = IAMAuthenticator(apikey)
-visual_recognition = VisualRecognitionV3(
-    version=version,
-    authenticator=authenticator
-)
-
-visual_recognition.set_service_url(url)
-
-
-visual_recognition.set_default_headers({'x-watson-learning-opt-out': "true"})
-
-
-data = [
-{
-  "title": "Bear Country, South Dakota",
-  "url": "https://example.com/photos/highres/20140717.jpg"
-},
-{
-  "title": "Pactola Lake",
-  "url": "https://example.com/photos/highres/20140718.jpg"
-},
-{
-  "title": "Welcome to Utah",
-  "url": "https://example.com/photos/highres/20190608_02.jpg"
-},
-{
-  "title": "Honey Badger",
-  "url": "https://example.com/photos/highres/20190611_03.jpg"
-},
-{
-  "title": "Grand Canyon Lizard",
-  "url": "https://example.com/photos/highres/20190612.jpg"
-},
-{
-  "title": "The Workhouse",
-  "url": "https://example.com/photos/highres/20191116_01.jpg"
-}
-]
-
-
-from ibm_watson import ApiException
-
-for x in range(len(data)):
-    try:
-        url = data[x]["url"]
-        images_filename = data[x]["title"]
-        classes = visual_recognition.classify(
-            url=url,
-            images_filename=images_filename,
-            threshold='0.6',
-            owners=["IBM"]).get_result()
-        print("-------------------------------------------------------------------------------------------------------------------------------------")
-        print("Image Title: ", data[x]["title"], "\n")
-        print("Image URL: ", data[x]["url"], "\n")
-        classification_results = classes["images"][0]["classifiers"][0]["classes"]
-        for result in classification_results:
-            print(result["class"], "(", result["score"], ")")
-        print("-------------------------------------------------------------------------------------------------------------------------------------")
-    except ApiException as ex:
-        print("Method failed with status code " + str(ex.code) + ": " + ex.message)
diff --git a/.virtual_documents/notebooks/TensorFlow_QuickStart.ipynb b/.virtual_documents/notebooks/TensorFlow_QuickStart.ipynb
deleted file mode 100644
index 5f6d4b3..0000000
--- a/.virtual_documents/notebooks/TensorFlow_QuickStart.ipynb
+++ /dev/null
@@ -1,82 +0,0 @@
-
-
-
-
-
-
-# pip3 install tensorflow
-
-
-import tensorflow as tf
-print("TensorFlow version:", tf.__version__)
-
-
-
-
-
-# Load and prepare the MNIST dataset. The pixel values of the images range from 0 through 255.
-# Scale these values to a range of 0 to 1 by dividing the values by 255.0.
-# This also converts the sample data from integers to floating-point numbers:
-mnist = tf.keras.datasets.mnist
-
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
-x_train, x_test = x_train / 255.0, x_test / 255.0
-
-
-# You can preview the raw data prior to training the model
-print(mnist.load_data())
-
-
-
-
-
-# Build a tf.keras.Sequential model:
-model = tf.keras.models.Sequential([
-  tf.keras.layers.Flatten(input_shape=(28, 28)),
-  tf.keras.layers.Dense(128, activation='relu'),
-  tf.keras.layers.Dropout(0.2),
-  tf.keras.layers.Dense(10)
-])
-
-
-# For each example, the model returns a vector of logits or log-odds scores, one for each class.
-predictions = model(x_train[:1]).numpy()
-predictions
-
-
-# The tf.nn.softmax function converts these logits to probabilities for each class: 
-tf.nn.softmax(predictions).numpy()
-
-
-# Define a loss function for training using losses.SparseCategoricalCrossentropy:
-loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-
-
-# Configure and compile the model
-model.compile(optimizer='adam',
-              loss=loss_fn,
-              metrics=['accuracy'])
-
-
-
-
-
-
-# Use the Model.fit method to adjust your model parameters and minimize the loss: 
-model.fit(x_train, y_train, epochs=5)
-
-
-# The Model.evaluate method checks the model's performance, usually on a validation set or test set.
-model.evaluate(x_test,  y_test, verbose=2)
-
-
-# If you want your model to return a probability, you can wrap the trained model, and attach the softmax to it:
-
-probability_model = tf.keras.Sequential([
-  model,
-  tf.keras.layers.Softmax()
-])
-probability_model(x_test[:5])
-
-
-
diff --git a/.virtual_documents/notebooks/Untitled.ipynb b/.virtual_documents/notebooks/Untitled.ipynb
deleted file mode 100644
index 7d6f130..0000000
--- a/.virtual_documents/notebooks/Untitled.ipynb
+++ /dev/null
@@ -1,47 +0,0 @@
-
-
-
-# pip3 install tensorflow
-
-
-import tensorflow as tf
-print("TensorFlow version:", tf.__version__)
-
-
-# Load and prepare the MNIST dataset. The pixel values of the images range from 0 through 255.
-# Scale these values to a range of 0 to 1 by dividing the values by 255.0.
-# This also converts the sample data from integers to floating-point numbers:
-mnist = tf.keras.datasets.mnist
-
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
-x_train, x_test = x_train / 255.0, x_test / 255.0
-
-
-# Build a tf.keras.Sequential model:
-model = tf.keras.models.Sequential([
-  tf.keras.layers.Flatten(input_shape=(28, 28)),
-  tf.keras.layers.Dense(128, activation='relu'),
-  tf.keras.layers.Dropout(0.2),
-  tf.keras.layers.Dense(10)
-])
-
-
-# For each example, the model returns a vector of logits or log-odds scores, one for each class.
-predictions = model(x_train[:1]).numpy()
-predictions
-
-
-# The tf.nn.softmax function converts these logits to probabilities for each class: 
-tf.nn.softmax(predictions).numpy()
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/notebooks/.ipynb_checkpoints/IBM Watson Visual Recognition-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/IBM Watson Visual Recognition-checkpoint.ipynb
deleted file mode 100644
index 86d5862..0000000
--- a/notebooks/.ipynb_checkpoints/IBM Watson Visual Recognition-checkpoint.ipynb
+++ /dev/null
@@ -1,209 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# IBM Watson Visual Recognition\n",
-    "Create an account on [IBM Watson Studio](https://www.ibm.com/cloud/watson-studio) and add the [Watson Visual Recognition](https://www.ibm.com/cloud/watson-visual-recognition) service to your free account."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install --upgrade --user \"ibm-watson>=4.5.0\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "apikey = \"<your-apikey>\"\n",
-    "version = \"2018-03-19\"\n",
-    "url = \"<your-url>\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "from ibm_watson import VisualRecognitionV3\n",
-    "from ibm_cloud_sdk_core.authenticators import IAMAuthenticator\n",
-    "\n",
-    "authenticator = IAMAuthenticator(apikey)\n",
-    "visual_recognition = VisualRecognitionV3(\n",
-    "    version=version,\n",
-    "    authenticator=authenticator\n",
-    ")\n",
-    "\n",
-    "visual_recognition.set_service_url(url)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "visual_recognition.set_default_headers({'x-watson-learning-opt-out': \"true\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = [\n",
-    "{\n",
-    "  \"title\": \"Bear Country, South Dakota\",\n",
-    "  \"url\": \"https://example.com/photos/highres/20140717.jpg\"\n",
-    "},\n",
-    "{\n",
-    "  \"title\": \"Pactola Lake\",\n",
-    "  \"url\": \"https://example.com/photos/highres/20140718.jpg\"\n",
-    "},\n",
-    "{\n",
-    "  \"title\": \"Welcome to Utah\",\n",
-    "  \"url\": \"https://example.com/photos/highres/20190608_02.jpg\"\n",
-    "},\n",
-    "{\n",
-    "  \"title\": \"Honey Badger\",\n",
-    "  \"url\": \"https://example.com/photos/highres/20190611_03.jpg\"\n",
-    "},\n",
-    "{\n",
-    "  \"title\": \"Grand Canyon Lizard\",\n",
-    "  \"url\": \"https://example.com/photos/highres/20190612.jpg\"\n",
-    "},\n",
-    "{\n",
-    "  \"title\": \"The Workhouse\",\n",
-    "  \"url\": \"https://example.com/photos/highres/20191116_01.jpg\"\n",
-    "}\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-------------------------------------------------------------------------------------------------------------------------------------\n",
-      "Image Title:  Bear Country, South Dakota \n",
-      "\n",
-      "brown bear ( 0.944 )\n",
-      "bear ( 1 )\n",
-      "carnivore ( 1 )\n",
-      "mammal ( 1 )\n",
-      "animal ( 1 )\n",
-      "Alaskan brown bear ( 0.759 )\n",
-      "greenishness color ( 0.975 )\n",
-      "-------------------------------------------------------------------------------------------------------------------------------------\n",
-      "-------------------------------------------------------------------------------------------------------------------------------------\n",
-      "Image Title:  Pactola Lake \n",
-      "\n",
-      "ponderosa pine ( 0.763 )\n",
-      "pine tree ( 0.867 )\n",
-      "tree ( 0.867 )\n",
-      "plant ( 0.867 )\n",
-      "blue color ( 0.959 )\n",
-      "-------------------------------------------------------------------------------------------------------------------------------------\n",
-      "-------------------------------------------------------------------------------------------------------------------------------------\n",
-      "Image Title:  Welcome to Utah \n",
-      "\n",
-      "signboard ( 0.953 )\n",
-      "building ( 0.79 )\n",
-      "blue color ( 0.822 )\n",
-      "purplish blue color ( 0.619 )\n",
-      "-------------------------------------------------------------------------------------------------------------------------------------\n",
-      "-------------------------------------------------------------------------------------------------------------------------------------\n",
-      "Image Title:  Honey Badger \n",
-      "\n",
-      "American badger ( 0.689 )\n",
-      "carnivore ( 0.689 )\n",
-      "mammal ( 0.864 )\n",
-      "animal ( 0.864 )\n",
-      "armadillo ( 0.618 )\n",
-      "light brown color ( 0.9 )\n",
-      "reddish brown color ( 0.751 )\n",
-      "-------------------------------------------------------------------------------------------------------------------------------------\n",
-      "-------------------------------------------------------------------------------------------------------------------------------------\n",
-      "Image Title:  Grand Canyon Lizard \n",
-      "\n",
-      "western fence lizard ( 0.724 )\n",
-      "lizard ( 0.93 )\n",
-      "reptile ( 0.93 )\n",
-      "animal ( 0.93 )\n",
-      "ultramarine color ( 0.633 )\n",
-      "-------------------------------------------------------------------------------------------------------------------------------------\n",
-      "-------------------------------------------------------------------------------------------------------------------------------------\n",
-      "Image Title:  The Workhouse \n",
-      "\n",
-      "castle ( 0.896 )\n",
-      "fortification ( 0.905 )\n",
-      "defensive structure ( 0.96 )\n",
-      "stronghold ( 0.642 )\n",
-      "building ( 0.799 )\n",
-      "mound ( 0.793 )\n",
-      "blue color ( 0.745 )\n",
-      "-------------------------------------------------------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "from ibm_watson import ApiException\n",
-    "\n",
-    "for x in range(len(data)):\n",
-    "    try:\n",
-    "        url = data[x][\"url\"]\n",
-    "        images_filename = data[x][\"title\"]\n",
-    "        classes = visual_recognition.classify(\n",
-    "            url=url,\n",
-    "            images_filename=images_filename,\n",
-    "            threshold='0.6',\n",
-    "            owners=[\"IBM\"]).get_result()\n",
-    "        print(\"-------------------------------------------------------------------------------------------------------------------------------------\")\n",
-    "        print(\"Image Title: \", data[x][\"title\"], \"\\n\")\n",
-    "        print(\"Image URL: \", data[x][\"url\"], \"\\n\")\n",
-    "        classification_results = classes[\"images\"][0][\"classifiers\"][0][\"classes\"]\n",
-    "        for result in classification_results:\n",
-    "            print(result[\"class\"], \"(\", result[\"score\"], \")\")\n",
-    "        print(\"-------------------------------------------------------------------------------------------------------------------------------------\")\n",
-    "    except ApiException as ex:\n",
-    "        print(\"Method failed with status code \" + str(ex.code) + \": \" + ex.message)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/.ipynb_checkpoints/TensorFlow_QuickStart-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/TensorFlow_QuickStart-checkpoint.ipynb
deleted file mode 100644
index 9323a3f..0000000
--- a/notebooks/.ipynb_checkpoints/TensorFlow_QuickStart-checkpoint.ipynb
+++ /dev/null
@@ -1,433 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "368ae4ce-f2d4-4d31-b4b6-4c95c01c472c",
-   "metadata": {},
-   "source": [
-    "# TensorFlow Quickstart\n",
-    "\n",
-    "Getting started with neural network machine learning models in TensorFlow."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f97fa6a5-b5db-45ac-98f4-54a4fee7ddaf",
-   "metadata": {},
-   "source": [
-    "## Set up TensorFlow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3ce17707-7c32-4ccf-8ef1-fbad5a78db7b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# pip3 install tensorflow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9e0d2030-33c0-4da7-bf65-919cdb3c113c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TensorFlow version: 2.13.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "print(\"TensorFlow version:\", tf.__version__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d23e7ecb-d531-426d-85dd-1d1d0f926439",
-   "metadata": {},
-   "source": [
-    "## Load a dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "d04bb60f-346b-44f5-bae8-16bb1cc60f87",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load and prepare the MNIST dataset. The pixel values of the images range from 0 through 255.\n",
-    "# Scale these values to a range of 0 to 1 by dividing the values by 255.0.\n",
-    "# This also converts the sample data from integers to floating-point numbers:\n",
-    "mnist = tf.keras.datasets.mnist\n",
-    "\n",
-    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-    "x_train, x_test = x_train / 255.0, x_test / 255.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "7895b2b4-3666-4a00-9536-8cdf4c4357da",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "((array([[[0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        ...,\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0]],\n",
-      "\n",
-      "       [[0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        ...,\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0]],\n",
-      "\n",
-      "       [[0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        ...,\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0]],\n",
-      "\n",
-      "       ...,\n",
-      "\n",
-      "       [[0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        ...,\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0]],\n",
-      "\n",
-      "       [[0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        ...,\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0]],\n",
-      "\n",
-      "       [[0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        ...,\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8), array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)), (array([[[0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        ...,\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0]],\n",
-      "\n",
-      "       [[0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        ...,\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0]],\n",
-      "\n",
-      "       [[0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        ...,\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0]],\n",
-      "\n",
-      "       ...,\n",
-      "\n",
-      "       [[0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        ...,\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0]],\n",
-      "\n",
-      "       [[0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        ...,\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0]],\n",
-      "\n",
-      "       [[0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        ...,\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0],\n",
-      "        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8), array([7, 2, 1, ..., 4, 5, 6], dtype=uint8)))\n"
-     ]
-    }
-   ],
-   "source": [
-    "# You can preview the raw data prior to training the model\n",
-    "print(mnist.load_data())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "82f07fd0-3341-4ac7-b2cc-ddc99802896f",
-   "metadata": {},
-   "source": [
-    "## Build a machine learning model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e3903d22-f584-4305-85a7-d7e1494cf909",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Build a tf.keras.Sequential model:\n",
-    "model = tf.keras.models.Sequential([\n",
-    "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
-    "  tf.keras.layers.Dense(128, activation='relu'),\n",
-    "  tf.keras.layers.Dropout(0.2),\n",
-    "  tf.keras.layers.Dense(10)\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "f4dd7df9-feb6-48b3-b332-4652812571d4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.28218323, -0.2626474 , -0.16938315,  0.15272117, -0.2957897 ,\n",
-       "        -0.0528494 ,  0.02909562,  0.06403146,  0.67431676, -0.35960984]],\n",
-       "      dtype=float32)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# For each example, the model returns a vector of logits or log-odds scores, one for each class.\n",
-    "predictions = model(x_train[:1]).numpy()\n",
-    "predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "b9a5a663-8d95-4fc5-a569-efb6362454e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.12565382, 0.07287167, 0.07999501, 0.1103954 , 0.07049612,\n",
-       "        0.08988202, 0.0975576 , 0.1010261 , 0.18598464, 0.0661376 ]],\n",
-       "      dtype=float32)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# The tf.nn.softmax function converts these logits to probabilities for each class: \n",
-    "tf.nn.softmax(predictions).numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "c11f1e4e-c6a8-4a65-a4cb-36486209797c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define a loss function for training using losses.SparseCategoricalCrossentropy:\n",
-    "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "b23213f0-7818-4c58-9672-495bc6bd240a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure and compile the model\n",
-    "model.compile(optimizer='adam',\n",
-    "              loss=loss_fn,\n",
-    "              metrics=['accuracy'])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74edfcf4-7b45-407f-8523-40cfab8cabc7",
-   "metadata": {},
-   "source": [
-    "## Train and evaluate your model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "c0438b2f-78f5-469f-a2f9-d31198ac6411",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/5\n",
-      "1875/1875 [==============================] - 1s 509us/step - loss: 0.3016 - accuracy: 0.9124\n",
-      "Epoch 2/5\n",
-      "1875/1875 [==============================] - 1s 514us/step - loss: 0.1462 - accuracy: 0.9572\n",
-      "Epoch 3/5\n",
-      "1875/1875 [==============================] - 1s 505us/step - loss: 0.1087 - accuracy: 0.9663\n",
-      "Epoch 4/5\n",
-      "1875/1875 [==============================] - 1s 512us/step - loss: 0.0893 - accuracy: 0.9718\n",
-      "Epoch 5/5\n",
-      "1875/1875 [==============================] - 1s 499us/step - loss: 0.0774 - accuracy: 0.9758\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<keras.src.callbacks.History at 0x2977640d0>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Use the Model.fit method to adjust your model parameters and minimize the loss: \n",
-    "model.fit(x_train, y_train, epochs=5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "7778bac2-ebd4-43eb-94a8-03b79152c58a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "313/313 - 0s - loss: 0.0790 - accuracy: 0.9757 - 126ms/epoch - 403us/step\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[0.07904709875583649, 0.9757000207901001]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# The Model.evaluate method checks the model's performance, usually on a validation set or test set.\n",
-    "model.evaluate(x_test,  y_test, verbose=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "96d86df4-4f22-4d76-ac4b-720192239015",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(5, 10), dtype=float32, numpy=\n",
-       "array([[2.1381442e-07, 1.2493059e-08, 4.6679975e-06, 5.0975598e-04,\n",
-       "        2.3767580e-10, 8.8744054e-07, 5.8283575e-13, 9.9947828e-01,\n",
-       "        4.8932998e-07, 5.7814891e-06],\n",
-       "       [1.6270951e-08, 3.1651885e-05, 9.9994957e-01, 1.1931742e-05,\n",
-       "        4.2942398e-15, 9.1026629e-07, 1.0364544e-06, 8.1141607e-17,\n",
-       "        4.9234400e-06, 7.9949551e-15],\n",
-       "       [1.7301611e-06, 9.9930012e-01, 5.5941098e-05, 2.8840779e-05,\n",
-       "        8.1860111e-05, 3.5271249e-05, 8.1873928e-05, 2.4437119e-04,\n",
-       "        1.6496866e-04, 5.0269696e-06],\n",
-       "       [9.9992669e-01, 4.8858471e-08, 1.1441392e-05, 2.0616257e-07,\n",
-       "        5.4289058e-07, 6.2358333e-07, 7.2935950e-06, 5.1983669e-05,\n",
-       "        3.5523688e-09, 1.0397144e-06],\n",
-       "       [4.4057975e-07, 7.7009216e-10, 7.9363446e-07, 5.2758939e-08,\n",
-       "        9.9748683e-01, 9.6599024e-08, 8.6932334e-07, 1.1146701e-05,\n",
-       "        5.3453311e-07, 2.4991999e-03]], dtype=float32)>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# If you want your model to return a probability, you can wrap the trained model, and attach the softmax to it:\n",
-    "\n",
-    "probability_model = tf.keras.Sequential([\n",
-    "  model,\n",
-    "  tf.keras.layers.Softmax()\n",
-    "])\n",
-    "probability_model(x_test[:5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ab098ffa-ab7d-4a76-90e0-255aa0763d22",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}