add .gitignore

1 files changed, 0 insertions, 433 deletions

diff --git a/notebooks/.ipynb_checkpoints/TensorFlow_QuickStart-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/TensorFlow_QuickStart-checkpoint.ipynb
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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
-}