cmc/data-science
Personal data science learning files.
clone: git clone https://gitbay.org/cmc/data-science.git
5e2bb53d528d60e0a44607377fa3d09553630d5b
unsigned
author: Christian Cleberg <hello@cleberg.net> · 2023-09-19T01:53:06Z
.../notebooks/IBM Watson Visual Recognition.ipynb | 75 ++++ .../notebooks/TensorFlow_QuickStart.ipynb | 82 ++++ .virtual_documents/notebooks/Untitled.ipynb | 47 +++ .../IBM Watson Visual Recognition-checkpoint.ipynb | 209 ++++++++++ .../TensorFlow_QuickStart-checkpoint.ipynb | 433 +++++++++++++++++++++ notebooks/IBM Watson Visual Recognition.ipynb | 4 +- notebooks/TensorFlow_QuickStart.ipynb | 433 +++++++++++++++++++++ 7 files changed, 1281 insertions(+), 2 deletions(-) new file mode 100644 @@ -0,0 +1,75 @@ + + + +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) new file mode 100644 @@ -0,0 +1,82 @@ + + + + + + +# 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]) + + + new file mode 100644 @@ -0,0 +1,47 @@ + + + +# 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() + + + + + + + + + + + + new file mode 100644 @@ -0,0 +1,209 @@ +{ + "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 +} new file mode 100644 @@ -0,0 +1,433 @@ +{ + "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 +} @@ -187,7 +187,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -201,7 +201,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.11.5" } }, "nbformat": 4, new file mode 100644 @@ -0,0 +1,433 @@ +{ + "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 +}