From 46ed4c00643fa5a3ffadfbbdaab9ea9c40cdea9e Mon Sep 17 00:00:00 2001 From: Luke Tyler Date: Fri, 15 Jan 2021 16:24:12 -0500 Subject: [PATCH 1/5] Fixed the minor naming convention errors --- .../deep_learning_basics-checkpoint.ipynb | 816 ++++++++++++++++++ .../deep_learning_basics.ipynb | 96 ++- .../images/mnist_dream_predicted.mp4 | Bin 0 -> 5702483 bytes 3 files changed, 874 insertions(+), 38 deletions(-) create mode 100644 tutorial_deep_learning_basics/.ipynb_checkpoints/deep_learning_basics-checkpoint.ipynb create mode 100644 tutorial_deep_learning_basics/images/mnist_dream_predicted.mp4 diff --git a/tutorial_deep_learning_basics/.ipynb_checkpoints/deep_learning_basics-checkpoint.ipynb b/tutorial_deep_learning_basics/.ipynb_checkpoints/deep_learning_basics-checkpoint.ipynb new file mode 100644 index 0000000..fe4a538 --- /dev/null +++ b/tutorial_deep_learning_basics/.ipynb_checkpoints/deep_learning_basics-checkpoint.ipynb @@ -0,0 +1,816 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![MIT Deep Learning](https://deeplearning.mit.edu/files/images/github/mit_deep_learning.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " \n", + " Visit MIT Deep Learning\n", + " Run in Google Colab\n", + " View Source on GitHub\n", + " Watch YouTube Videos
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "# Deep Learning Basics\n", + "\n", + "This tutorial accompanies the [lecture on Deep Learning Basics](https://www.youtube.com/watch?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&v=O5xeyoRL95U) given as part of [MIT Deep Learning](https://deeplearning.mit.edu). Acknowledgement to amazing people involved is provided throughout the tutorial and at the end. You can watch the video on YouTube:\n", + "\n", + "[![Deep Learning Basics](https://i.imgur.com/FfQVV8q.png)](https://www.youtube.com/watch?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&v=O5xeyoRL95U)\n", + "\n", + "In this tutorial, we mention seven important types/concepts/approaches in deep learning, introducing the first 2 and providing pointers to tutorials on the others. Here is a visual representation of the seven:\n", + "\n", + "![Deep learning concepts](https://i.imgur.com/EAl47rp.png)\n", + "\n", + "At a high-level, neural networks are either encoders, decoders, or a combination of both. Encoders find patterns in raw data to form compact, useful representations. Decoders generate new data or high-resolution useful infomation from those representations. As the lecture describes, deep learning discovers ways to **represent** the world so that we can reason about it. The rest is clever methods that help use deal effectively with visual information, language, sound (#1-6) and even act in a world based on this information and occasional rewards (#7).\n", + "\n", + "1. **Feed Forward Neural Networks (FFNNs)** - classification and regression based on features. See [Part 1](#Part-1:-Boston-Housing-Price-Prediction-with-Feed-Forward-Neural-Networks) of this tutorial for an example.\n", + "2. **Convolutional Neural Networks (CNNs)** - image classification, object detection, video action recognition, etc. See [Part 2](#Part-2:-Classification-of-MNIST-Dreams-with-Convolution-Neural-Networks) of this tutorial for an example.\n", + "3. **Recurrent Neural Networks (RNNs)** - language modeling, speech recognition/generation, etc. See [this TF tutorial on text generation](https://www.tensorflow.org/tutorials/sequences/text_generation) for an example.\n", + "4. **Encoder Decoder Architectures** - semantic segmentation, machine translation, etc. See [our tutorial on semantic segmentation](https://github.com/lexfridman/mit-deep-learning/blob/master/tutorial_driving_scene_segmentation/tutorial_driving_scene_segmentation.ipynb) for an example.\n", + "5. **Autoencoder** - unsupervised embeddings, denoising, etc.\n", + "6. **Generative Adversarial Networks (GANs)** - unsupervised generation of realistic images, etc. See [this TF tutorial on DCGANs](https://github.com/tensorflow/tensorflow/blob/r1.11/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb) for an example.\n", + "7. **Deep Reinforcement Learning** - game playing, robotics in simulation, self-play, neural arhitecture search, etc. We'll be releasing notebooks on this soon and will link them here.\n", + "\n", + "There are selective omissions and simplifications throughout these tutorials, hopefully without losing the essence of the underlying ideas. See Einstein quote..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 0: Prerequisites:\n", + "\n", + "We recommend that you run this this notebook in the cloud on Google Colab (see link with icon at the top) if you're not already doing so. It's the simplest way to get started. You can also [install TensorFlow locally](https://www.tensorflow.org/install/). But, again, simple is best (with caveats):\n", + "\n", + "![Einstein](https://i.imgur.com/vfPDHGN.png)\n", + "\n", + "[tf.keras](https://www.tensorflow.org/guide/keras) is the simplest way to build and train neural network models in TensorFlow. So, that's what we'll stick with in this tutorial, unless the models neccessitate a lower-level API.\n", + "\n", + "Note that there's [tf.keras](https://www.tensorflow.org/guide/keras) (comes with TensorFlow) and there's [Keras](https://keras.io/) (standalone). You should be using [tf.keras](https://www.tensorflow.org/guide/keras) because (1) it comes with TensorFlow so you don't need to install anything extra and (2) it comes with powerful TensorFlow-specific features." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.4.0\n" + ] + } + ], + "source": [ + "# TensorFlow and tf.keras\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense\n", + "\n", + "# Commonly used modules\n", + "import numpy as np\n", + "import os\n", + "import sys\n", + "\n", + "# Images, plots, display, and visualization\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import cv2\n", + "import IPython\n", + "from six.moves import urllib\n", + "\n", + "print(tf.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 1: Boston Housing Price Prediction with Feed Forward Neural Networks\n", + "\n", + "Let's start with using a fully-connected neural network to do predict housing prices. The following image highlights the difference between regression and classification (see part 2). Given an observation as input, **regression** outputs a continuous value (e.g., exact temperature) and classificaiton outputs a class/category that the observation belongs to.\n", + "\n", + "\"classification_regression\"\n", + "\n", + "For the Boston housing dataset, we get 506 rows of data, with 13 features in each. Our task is to build a regression model that takes these 13 features as input and output a single value prediction of the \"median value of owner-occupied homes (in $1000).\"\n", + "\n", + "Now, we load the dataset. Loading the dataset returns four NumPy arrays:\n", + "\n", + "* The `train_images` and `train_labels` arrays are the *training set*—the data the model uses to learn.\n", + "* The model is tested against the *test set*, the `test_images`, and `test_labels` arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "(train_features, train_labels), (test_features, test_labels) = keras.datasets.boston_housing.load_data()\n", + "\n", + "# get per-feature statistics (mean, standard deviation) from the training set to normalize by\n", + "train_mean = np.mean(train_features, axis=0)\n", + "train_std = np.std(train_features, axis=0)\n", + "train_features = (train_features - train_mean) / train_std" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "### Build the model\n", + "\n", + "Building the neural network requires configuring the layers of the model, then compiling the model. First we stack a few layers together using `keras.Sequential`. Next we configure the loss function, optimizer, and metrics to monitor. These are added during the model's compile step:\n", + "\n", + "* *Loss function* - measures how accurate the model is during training, we want to minimize this with the optimizer.\n", + "* *Optimizer* - how the model is updated based on the data it sees and its loss function.\n", + "* *Metrics* - used to monitor the training and testing steps.\n", + "\n", + "Let's build a network with 1 hidden layer of 20 neurons, and use mean squared error (MSE) as the loss function (most common one for regression problems):" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "" + }, + "outputs": [], + "source": [ + "def build_model():\n", + " model = keras.Sequential([\n", + " Dense(20, activation=tf.nn.relu, input_shape=[len(train_features[0])]),\n", + " Dense(1)\n", + " ])\n", + "\n", + " model.compile(optimizer=tf.train.AdamOptimizer(), \n", + " loss='mse',\n", + " metrics=['mae', 'mse'])\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "### Train the model\n", + "\n", + "Training the neural network model requires the following steps:\n", + "\n", + "1. Feed the training data to the model—in this example, the `train_features` and `train_labels` arrays.\n", + "2. The model learns to associate features and labels.\n", + "3. We ask the model to make predictions about a test set—in this example, the `test_features` array. We verify that the predictions match the labels from the `test_labels` array. \n", + "\n", + "To start training, call the `model.fit` method—the model is \"fit\" to the training data:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "" + }, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'tensorflow._api.v2.train' has no attribute 'AdamOptimizer'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbuild_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mearly_stop\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mEarlyStopping\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmonitor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'val_loss'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpatience\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36mbuild_model\u001b[1;34m()\u001b[0m\n\u001b[0;32m 5\u001b[0m ])\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m model.compile(optimizer=tf.train.AdamOptimizer(), \n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'mse'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m metrics=['mae', 'mse'])\n", + "\u001b[1;31mAttributeError\u001b[0m: module 'tensorflow._api.v2.train' has no attribute 'AdamOptimizer'" + ] + } + ], + "source": [ + "# this helps makes our output less verbose but still shows progress\n", + "class PrintDot(keras.callbacks.Callback):\n", + " def on_epoch_end(self, epoch, logs):\n", + " if epoch % 100 == 0: print('')\n", + " print('.', end='')\n", + "\n", + "model = build_model()\n", + "\n", + "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=50)\n", + "history = model.fit(train_features, train_labels, epochs=1000, verbose=0, validation_split = 0.1,\n", + " callbacks=[early_stop, PrintDot()])\n", + "\n", + "hist = pd.DataFrame(history.history)\n", + "hist['epoch'] = history.epoch\n", + "\n", + "# show RMSE measure to compare to Kaggle leaderboard on https://www.kaggle.com/c/boston-housing/leaderboard\n", + "rmse_final = np.sqrt(float(hist['val_mean_squared_error'].tail(1)))\n", + "print()\n", + "print('Final Root Mean Square Error on validation set: {}'.format(round(rmse_final, 3)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's plot the loss function measure on the training and validation sets. The validation set is used to prevent overfitting ([learn more about it here](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit)). However, because our network is small, the training convergence without noticeably overfitting the data as the plot shows." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def plot_history():\n", + " plt.figure()\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Mean Square Error [Thousand Dollars$^2$]')\n", + " plt.plot(hist['epoch'], hist['mean_squared_error'], label='Train Error')\n", + " plt.plot(hist['epoch'], hist['val_mean_squared_error'], label = 'Val Error')\n", + " plt.legend()\n", + " plt.ylim([0,50])\n", + "\n", + "plot_history()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "Next, compare how the model performs on the test dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "102/102 [==============================] - 0s 44us/step\n", + "Root Mean Square Error on test set: 4.244\n" + ] + } + ], + "source": [ + "test_features_norm = (test_features - train_mean) / train_std\n", + "mse, _, _ = model.evaluate(test_features_norm, test_labels)\n", + "rmse = np.sqrt(mse)\n", + "print('Root Mean Square Error on test set: {}'.format(round(rmse, 3)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "Compare the RMSE measure you get to the [Kaggle leaderboard](https://www.kaggle.com/c/boston-housing/leaderboard). An RMSE of 2.651 puts us in 5th place." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 2: Classification of MNIST Dreams with Convolutional Neural Networks\n", + "\n", + "Next, let's build a convolutional neural network (CNN) classifier to classify images of handwritten digits in the MNIST dataset with a twist where we test our classifier on high-resolution hand-written digits from outside the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Set common constants\n", + "this_repo_url = 'https://github.com/lexfridman/mit-deep-learning/raw/master/'\n", + "this_tutorial_url = this_repo_url + 'tutorial_deep_learning_basics'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "The MNIST dataset containss 70,000 grayscale images of handwritten digits at a resolution of 28 by 28 pixels. The task is to take one of these images as input and predict the most likely digit contained in the image (along with a relative confidence in this prediction):\n", + "\n", + "\n", + "\n", + "Now, we load the dataset. The images are 28x28 NumPy arrays, with pixel values ranging between 0 and 255. The *labels* are an array of integers, ranging from 0 to 9." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "" + }, + "outputs": [], + "source": [ + "(train_images, train_labels), (test_images, test_labels) = keras.datasets.mnist.load_data()\n", + "\n", + "# reshape images to specify that it's a single channel\n", + "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)\n", + "test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "We scale these values to a range of 0 to 1 before feeding to the neural network model. For this, we divide the values by 255. It's important that the *training set* and the *testing set* are preprocessed in the same way:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "" + }, + "outputs": [], + "source": [ + "def preprocess_images(imgs): # should work for both a single image and multiple images\n", + " sample_img = imgs if len(imgs.shape) == 2 else imgs[0]\n", + " assert sample_img.shape in [(28, 28, 1), (28, 28)], sample_img.shape # make sure images are 28x28 and single-channel (grayscale)\n", + " return imgs / 255.0\n", + "\n", + "train_images = preprocess_images(train_images)\n", + "test_images = preprocess_images(test_images)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "Display the first 5 images from the *training set* and display the class name below each image. Verify that the data is in the correct format and we're ready to build and train the network." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,2))\n", + "for i in range(5):\n", + " plt.subplot(1,5,i+1)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.grid(False)\n", + " plt.imshow(train_images[i].reshape(28, 28), cmap=plt.cm.binary)\n", + " plt.xlabel(train_labels[i])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "### Build the model\n", + "\n", + "Building the neural network requires configuring the layers of the model, then compiling the model. In many cases, this can be reduced to simply stacking together layers:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "" + }, + "outputs": [], + "source": [ + "model = keras.Sequential()\n", + "# 32 convolution filters used each of size 3x3\n", + "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n", + "# 64 convolution filters used each of size 3x3\n", + "model.add(Conv2D(64, (3, 3), activation='relu'))\n", + "# choose the best features via pooling\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "# randomly turn neurons on and off to improve convergence\n", + "model.add(Dropout(0.25))\n", + "# flatten since too many dimensions, we only want a classification output\n", + "model.add(Flatten())\n", + "# fully connected to get all relevant data\n", + "model.add(Dense(128, activation='relu'))\n", + "# one more dropout\n", + "model.add(Dropout(0.5))\n", + "# output a softmax to squash the matrix into output probabilities\n", + "model.add(Dense(10, activation='softmax'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "Before the model is ready for training, it needs a few more settings. These are added during the model's *compile* step:\n", + "\n", + "* *Loss function* - measures how accurate the model is during training, we want to minimize this with the optimizer.\n", + "* *Optimizer* - how the model is updated based on the data it sees and its loss function.\n", + "* *Metrics* - used to monitor the training and testing steps. \"accuracy\" is the fraction of images that are correctly classified." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "" + }, + "outputs": [], + "source": [ + "model.compile(optimizer=tf.train.AdamOptimizer(), \n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "### Train the model\n", + "\n", + "Training the neural network model requires the following steps:\n", + "\n", + "1. Feed the training data to the model—in this example, the `train_images` and `train_labels` arrays.\n", + "2. The model learns to associate images and labels.\n", + "3. We ask the model to make predictions about a test set—in this example, the `test_images` array. We verify that the predictions match the labels from the `test_labels` array. \n", + "\n", + "To start training, call the `model.fit` method—the model is \"fit\" to the training data:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "60000/60000 [==============================] - 7s 121us/step - loss: 0.1953 - acc: 0.9410\n", + "Epoch 2/5\n", + "60000/60000 [==============================] - 6s 100us/step - loss: 0.0842 - acc: 0.9753\n", + "Epoch 3/5\n", + "60000/60000 [==============================] - 6s 96us/step - loss: 0.0642 - acc: 0.9810\n", + "Epoch 4/5\n", + "60000/60000 [==============================] - 6s 94us/step - loss: 0.0526 - acc: 0.9835\n", + "Epoch 5/5\n", + "60000/60000 [==============================] - 6s 94us/step - loss: 0.0443 - acc: 0.9861\n" + ] + } + ], + "source": [ + "history = model.fit(train_images, train_labels, epochs=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 98.68% on the training data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "### Evaluate accuracy\n", + "\n", + "Next, compare how the model performs on the test dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10000, 28, 28, 1)\n", + "10000/10000 [==============================] - 1s 50us/step\n", + "Test accuracy: 0.9913\n" + ] + } + ], + "source": [ + "print(test_images.shape)\n", + "test_loss, test_acc = model.evaluate(test_images, test_labels)\n", + "\n", + "print('Test accuracy:', test_acc)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "Often times, the accuracy on the test dataset is a little less than the accuracy on the training dataset. This gap between training accuracy and test accuracy is an example of *overfitting*. In our case, the accuracy is better at 99.19%! This is, in part, due to successful regularization accomplished with the Dropout layers." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "### Make predictions\n", + "\n", + "With the model trained, we can use it to make predictions about some images. Let's step outside the MNIST dataset for that and go with the beautiful high-resolution images generated by a mixture of CPPN, GAN, VAE. See [great blog post by hardmaru](http://blog.otoro.net/2016/04/01/generating-large-images-from-latent-vectors/) for the source data and a description of how these morphed animations are generated:\n", + "\n", + "![MNIST dream](https://i.imgur.com/OrUJs9V.gif)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mnist_dream_path = 'images/mnist_dream.mp4'\n", + "mnist_prediction_path = 'images/mnist_dream_predicted.mp4'\n", + "\n", + "# download the video if running in Colab\n", + "if not os.path.isfile(mnist_dream_path): \n", + " print('downloading the sample video...')\n", + " vid_url = this_tutorial_url + '/' + mnist_dream_path\n", + " \n", + " mnist_dream_path = urllib.request.urlretrieve(vid_url)[0]\n", + " \n", + "def cv2_imshow(img):\n", + " ret = cv2.imencode('.png', img)[1].tobytes() \n", + " img_ip = IPython.display.Image(data=ret)\n", + " IPython.display.display(img_ip)\n", + "\n", + "cap = cv2.VideoCapture(mnist_dream_path) \n", + "vw = None\n", + "frame = -1 # counter for debugging (mostly), 0-indexed\n", + "\n", + "# go through all the frames and run our classifier on the high res MNIST images as they morph from number to number\n", + "while True: # should 481 frames\n", + " frame += 1\n", + " ret, img = cap.read()\n", + " if not ret: break\n", + " \n", + " assert img.shape[0] == img.shape[1] # should be a square\n", + " if img.shape[0] != 720:\n", + " img = cv2.resize(img, (720, 720))\n", + " \n", + " #preprocess the image for prediction\n", + " img_proc = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", + " img_proc = cv2.resize(img_proc, (28, 28))\n", + " img_proc = preprocess_images(img_proc)\n", + " img_proc = 1 - img_proc # inverse since training dataset is white text with black background\n", + "\n", + " net_in = np.expand_dims(img_proc, axis=0) # expand dimension to specify batch size of 1\n", + " net_in = np.expand_dims(net_in, axis=3) # expand dimension to specify number of channels\n", + " \n", + " preds = model.predict(net_in)[0]\n", + " guess = np.argmax(preds)\n", + " perc = np.rint(preds * 100).astype(int)\n", + " \n", + " img = 255 - img\n", + " pad_color = 0\n", + " img = np.pad(img, ((0,0), (0,1280-720), (0,0)), mode='constant', constant_values=(pad_color)) \n", + " \n", + " line_type = cv2.LINE_AA\n", + " font_face = cv2.FONT_HERSHEY_SIMPLEX\n", + " font_scale = 1.3 \n", + " thickness = 2\n", + " x, y = 740, 60\n", + " color = (255, 255, 255)\n", + " \n", + " text = \"Neural Network Output:\"\n", + " cv2.putText(img, text=text, org=(x, y), fontScale=font_scale, fontFace=font_face, thickness=thickness,\n", + " color=color, lineType=line_type)\n", + " \n", + " text = \"Input:\"\n", + " cv2.putText(img, text=text, org=(30, y), fontScale=font_scale, fontFace=font_face, thickness=thickness,\n", + " color=color, lineType=line_type) \n", + " \n", + " y = 130\n", + " for i, p in enumerate(perc):\n", + " if i == guess: color = (255, 218, 158)\n", + " else: color = (100, 100, 100)\n", + " \n", + " rect_width = 0\n", + " if p > 0: rect_width = int(p * 3.3)\n", + " \n", + " rect_start = 180\n", + " cv2.rectangle(img, (x+rect_start, y-5), (x+rect_start+rect_width, y-20), color, -1)\n", + "\n", + " text = '{}: {:>3}%'.format(i, int(p))\n", + " cv2.putText(img, text=text, org=(x, y), fontScale=font_scale, fontFace=font_face, thickness=thickness,\n", + " color=color, lineType=line_type)\n", + " y += 60\n", + " \n", + " # if you don't want to save the output as a video, set this to False\n", + " save_video = True\n", + " \n", + " if save_video:\n", + " if vw is None:\n", + " codec = cv2.VideoWriter_fourcc(*'DIVX')\n", + " vid_width_height = img.shape[1], img.shape[0]\n", + " vw = cv2.VideoWriter(mnist_prediction_path, codec, 30, vid_width_height)\n", + " # 15 fps above doesn't work robustly so we right frame twice at 30 fps\n", + " vw.write(img)\n", + " vw.write(img)\n", + " \n", + " # scale down image for display\n", + " img_disp = cv2.resize(img, (0,0), fx=0.5, fy=0.5)\n", + " cv2_imshow(img_disp)\n", + " IPython.display.clear_output(wait=True)\n", + " \n", + "cap.release()\n", + "if vw is not None:\n", + " vw.release()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "" + }, + "source": [ + "The above shows the prediction of the network by choosing the neuron with the highest output. While the output layer values add 1 to one, these do not reflect well-calibrated measures of \"uncertainty\". Often, the network is overly confident about the top choice that does not reflect a learned measure of probability. If everything ran correctly you should get an animation like this:\n", + "\n", + "![MNIST dream predictions](https://i.imgur.com/eMF9FOG.gif)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Acknowledgements\n", + "\n", + "The contents of this tutorial is based on and inspired by the work of [TensorFlow team](https://www.tensorflow.org) (see their [Colab notebooks](https://www.tensorflow.org/tutorials/)), our [MIT Human-Centered AI team](https://hcai.mit.edu), and individual pieces referenced in the [MIT Deep Learning](https://deeplearning.mit.edu) course slides." + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "tutorial_deep_learning_basics.ipynb", + "provenance": [], + "toc_visible": true, + "version": "0.3.2" + }, + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tutorial_deep_learning_basics/deep_learning_basics.ipynb b/tutorial_deep_learning_basics/deep_learning_basics.ipynb index 2b4f48f..f4af5fb 100644 --- a/tutorial_deep_learning_basics/deep_learning_basics.ipynb +++ b/tutorial_deep_learning_basics/deep_learning_basics.ipynb @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", @@ -87,7 +87,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.12.0\n" + "2.4.0\n" ] } ], @@ -133,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "metadata": { "colab": {}, "colab_type": "code", @@ -179,7 +179,7 @@ " Dense(1)\n", " ])\n", "\n", - " model.compile(optimizer=tf.train.AdamOptimizer(), \n", + " model.compile(optimizer = tf.keras.optimizers.Adam(), \n", " loss='mse',\n", " metrics=['mae', 'mse'])\n", " return model" @@ -205,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 19, "metadata": { "colab": {}, "colab_type": "code", @@ -223,9 +223,19 @@ "....................................................................................................\n", "....................................................................................................\n", "....................................................................................................\n", - "....................................................................................................\n", - ".............................................................................................\n", - "Final Root Mean Square Error on validation set: 2.359\n" + "......................................... loss mae mse val_loss val_mae val_mse epoch\n", + "631 7.206632 1.862269 7.206632 6.058085 2.016366 6.058085 631\n", + "632 7.225244 1.866688 7.225244 6.192718 2.032652 6.192718 632\n", + "633 7.203722 1.859851 7.203722 6.200646 2.047698 6.200646 633\n", + "634 7.196043 1.857011 7.196043 6.146395 2.037326 6.146395 634\n", + "635 7.212521 1.861273 7.212521 6.356153 2.059154 6.356153 635\n", + "636 7.187393 1.858825 7.187393 6.267227 2.054133 6.267227 636\n", + "637 7.210217 1.858126 7.210217 6.092490 2.025726 6.092490 637\n", + "638 7.187838 1.855549 7.187838 6.252947 2.046569 6.252947 638\n", + "639 7.183084 1.855572 7.183084 6.196176 2.038631 6.196176 639\n", + "640 7.186580 1.857012 7.186580 6.435256 2.077744 6.435256 640\n", + "\n", + "Final Root Mean Square Error on validation set: 2.537\n" ] } ], @@ -246,7 +256,8 @@ "hist['epoch'] = history.epoch\n", "\n", "# show RMSE measure to compare to Kaggle leaderboard on https://www.kaggle.com/c/boston-housing/leaderboard\n", - "rmse_final = np.sqrt(float(hist['val_mean_squared_error'].tail(1)))\n", + "print(hist.tail(10))\n", + "rmse_final = np.sqrt(float(hist['val_mse'].tail(1)))\n", "print()\n", "print('Final Root Mean Square Error on validation set: {}'.format(round(rmse_final, 3)))" ] @@ -260,14 +271,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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6iJhY145weAkh1cd+KyEYYyJQoAmhrUpvqxBvTXLWkSUEqzIyxkSoQHsZjW1lVlPBDS4zLUnKOqKE0DPNR3FlHdV1fhLjY8MUmDHGHCnQBXLsm6s9krOgvOCQQ317JAGwp6SaQTkp4YjKGGNaZAvkdKSkLKgsOuRQnx6uQLWruCocERljTKssIXSkFtoQ+nklhJ2WEIwxEcYSQkdKyoLacqg/2O7eO8OVEHaXVIcrKmOMaVFAbQgi8q22zntzHJnDJTcbrZzWGwBfXCw5qT6rMjLGRJxAexmlec8jgJOBF7z9i4EPQh1Ul9F8tLKXEAD69Ui0KiNjTMQJtJfRvQAi8g4wQVXLvP17gJc6LLpol5ztnisPHdPXJyOJjXvLwhCQMca0Ltg2hF4cOhCt1jtmWpLirehWcejEsH17JLG7pBpttrymMcaEW7DTXz8FfCAiC3CD0mYCT4Y8qq4itad7Lj88ISRSWeunpKqOHskJYQjMGGOOFFRCUNWfiMgrwFRAcQvkrOyQyLqCpCyQ2BZLCOC6nlpCMMZEiqCqjETEB4wEUoAewMW2JkIbYmIgJQcq9h5yuDEh7C62rqfGmMgRbJXR80AJsBywGdoCkdLziCqjxsFp+UWV4YjIGGNaFGxCyFPV8zokkq6qhRJCTmoCqb44tu6vCFNQxhhzpGB7GS0WkdEdEklXldrziDYEEWFwTgpbLCEYYyJIsCWE04HrRGQrrspIAFXVMSGPrKtIyXVVRqog0nR4SG4Ky7YVtfFCY4zpXMEmhPM7JIquLLUn1Fe5OY18aU2HB+ek8MJHu2xdBGNMxAi22+n2jgqky2ocnFa+95CEMCQ3FVXYVljByN7pYQrOGGMOCraEgIhkAsNotlKaqr4TyqC6lLQ+7rlsN2Qf13R4iLc4zpZ9lhCMMZEhqIQgIl8GbgPygFXAqcASYEbII+sqMvq755Kdhxwe7CUE62lkjIkUwfYyug032+l2VZ0OjAeKQx1Ul5Le1z2X7DjkcIovjl7pPjbvKw9DUMYYc6RgE0K1qlaDG7WsqutxU2Kb1iQku1lPS/KPODUkJ5Ut+6yEYIyJDMEmhHwR6QH8C3hDRJ4HrKH5aDLyoHTnEYeH90plY0EZDQ0266kxJvyCSgiqeomqFqvqPcCPgMeAWR0QV9eSntdiCWFU3wwqav1sP2BTWBhjwi/Yye0uF5HGvpNnANcDo0IeVVeT0XJCOKGv6120ZldJZ0dkjDFHCLbK6EeqWiYip+N6Fj0O/CH0YXUxGXlQUwrVh37xD+uVSlyMsHZXaZgCM8aYg4JNCH7v+ULg/1T1JcAm9D+ajDz3fFjXU19cLMN6pbHGEoIxJgIEmxB2isgfgauAl731EYK9R/fTlBCOrDYa3S+dj/OLbTlNY0zYBftlfgXwGvA5VS0GsoDvhjqoLqcxIZQemRDGD8ikqLKO7YXWsGyMCa9gp674jvd8ojSbuRN4PdAbiEgi8A7g897/76r6YxEZDMwHsnEL8FyjqrVBxheZUntBTFyLJYTxA3oAsHJHEYO80cvGGBMOwZYQKpo9/LjZTwcFeY8aYIaqjgXGAeeJyKnAA8BvVHUoUATcGOR9I1dMrBux3EJCGNYzjZSEWFZ9Vtz5cRljTDPBznb6q+b7IvJLXBVSMPdQoHG+hnjvobheS1/0jj8J3AM8Gsy9I1orYxFiY4TxAzJZvLkwDEEZY8xBx9ognIyb6C4oIhIrIquAvcAbwGagWFXrvUvygX6tvPYmEVkmIsv27dvX0iWRqZWxCAAzRvZk495ythfaNBbGmPAJdmDaahH52HusATYADwb7pqrqV9VxuGRyCjAyiNfOVdWJqjoxNzc32LcOn4w8KN0FDf4jTp1zQi8A3lhb0NlRGWNMk2AblS9qtl0PFDT7VR80VS0WkYXAZKCHiMR598sDjpz8J5r16A8NdW5dhIxDC1X9s5IZ2TuN19cW8OWpQ8IUoDGmuwt2LqPtQA/gYuAS4IRg31BEcr0J8hCRJOAcYB2wELjMu2wO8Hyw945oWd7iOIWbWzz9uRN6sWzbAQ5UdI2OVcaY6BNsldFtwDygp/eYJyJfD/I9+wALReRj4EPgDVV9EbgT+JaIbMJ1PX08yPtGtsbV0g60nBDOOaE3DQoL1+/txKCMMeagYKuMbgQmqWoFgIg8gFsx7XeB3kBVP8YtrHP48S249oSuKa0vxCW1WkI4sV86vdMTeWNtAZeeFHQ7vTHGHLNgexkJB+czwtuWVq41zcXEQNYQKNzU4mkR4ewTevLOxn1U1x3Z8GyMMR0t2ITwBPC+iNwjIvcAS+lqVTsdKfu4VhMCuGqjylo/izfv78SgjDHGCbZR+de4NRAOeI/rVfXBDoira8oeCkXbwF/X4ulTh2SR6ovjldV7OjcuY4yhHQPTVHWFqj7kPVZ2RFBdVq9R0FAPe9e1eNoXF8tFY/rw0urdlNe0uzevMca0S7C9jHwi8kUR+b6I3N346Kjgupy+Xlv6rhWtXnLFyf2prPXz7492dVJQxhjjBFtCeB6YiRuU1nyiOxOIrCGQmAG7Wi9Yje/fg5G903h66XZbI8EY06mC7Xaap6rndUgk3YGIKyW0kRBEhGsnD+L7C1az4rMiThqY1YkBGmO6s2BLCItFZHSHRNJd9J0ABWugtvUFcWaN70taYhx/Xry9EwMzxnR3ASWExkntgNOBFSKywZvgrvG4CdTA01zDcv4HrV6SnBDHlRP78/Lq3WzZV97qdcYYE0qBlhC+gJu/6HxgKPA5b/8i79kEqv8kkBjY9t82L/vKmceRGBfDL17b0EmBGWO6u0DbEJ5V1QkdGkl3kZgOfcbC9rYTQm6aj/85YwgPvrmRFZ8VMWFAZicFaIzprgItIdj0FKE0+EzY8T5UtD0i+X+mDiEn1cf9L6+3HkfGmA4XaELIFZFvtfbo0Ai7ojFXuHaE1X9v87IUXxy3nT2MD7Yd4K11NguqMaZjBZoQYoFUIK2VhwlGr1HQZxysmnfUS686uT9DclJ44NX11PsbOj42Y0y3FWgbwm5Vva9DI+luxl0Nr3wX9qyG3q335I2PjeGO80Zw89MreHrpdq6bMrgTgzTGdCfWhhAuoy+DmHhY9cxRLz13VG/OGJ7LL17bwM7iqk4IzhjTHQWaEM7q0Ci6o+QsGHE+fPxsq7OfNhIRfnrJiSjwgwWrrYHZGNMhAk0Ibx7tAhFpfcY207JxV0PlftjwylEvzctM5rvnjmDRhn08v8omvjPGhF6gbQjHH2VEsgAZIYinexl6NmQMgEX3w3EzwJfa5uXXTh7ECx/t4t5/r2HqsByyU32dFKgxpjsItIQwEjciubXHRcBpHRFglxYbBxf8HPaugSW/P/rlMcIDl46hvKae+15c2wkBGmO6k4BKCKpqs6x1lBHnwwmz4O0HoLoEzr4H4lr/5T+8Vxq3Th/Kg29u5Ozje3Hx2L6dFqoxpmsLdvpr0xFmPeKSwNJHQBvgvPvdVNmtuHX6UN75dB93/XM1Y/IyGJid0onBGmO6qoCnvxanf0cG020lpMAX5sKkm+H9P8Dq59q8PD42hodmjydG4OvPrKTOBqwZY0Ig4ISgrq/jyx0Yizn3p9D/VPj37bDl7TYvzctM5oFLx/Bxfgl/fHtz58RnjOnSgl0gZ4WInNwhkRiIiYXL/gTpfWDBzbBzeZuXnz+6DxeO6cODb25k8aa2J8ozxpijCTYhTAKWiMhmWyCng2T0g0sfA38t/N8MeOwcWPgzKN/X4uU/+8JohuSm8JWnl7Npb1knB2uM6UokmFGvIjKwpePh6oU0ceJEXbZsWTjeuuNVl8KyP8H7f4SyXZCUBTN/DyMuOKLBOb+oklkPLyYxPoZ/3TqFHBufYIxphYgsV9WJLZ0LqoTgffH34OD4gx7WJbWDJKbD6bfDt9fB9a+4XkjzvwivfR8OS+J5mck8Nmci+8tr+J+nllFd5w9PzMaYqBZUQhCR24B5QE/v8bSIfL0jAjPNDDwNblkMY7/ouqa+de8RSWFc/x48eOU4Vu0o5tt/+4iGBpvvyBgTnGDHIdwITFLVCgAReQBYAvwu1IGZwyRnHRyv8N5vID4FzvzuIZecd2If7jp/JD99eT0Ds5O547yRYQrWGBONgk0IAjSvj/BjU2N3HhG48NdQVwUL/xdW/gWSesCkW2DcbMAtu7l1fyWPLNrMwOxkrjx5QHhjNsZEjWATwhPA+yKywNufBTwe0ohM22JiYObDkN7XNTgXb4cXvg59x0HP4xER7ps5ivyiSn6w4BP6ZCRxxvDccEdtjIkCAfcyEhEB8oBc4HTv8LuqurKDYjuqLt3LKBD1NVB5AB6dDBIDX34TsoYAUFpdxxV/WMKWfRU8NHs8553YO8zBGmMiQUh6GTWOVFbVFar6kPcIWzIwuPaE9D5w4xtuDqRnvgg15QCkJ8Yz/6ZTGdUvna/OW86zH34W5mCNMZHORip3BTnD4AuPwb518LN+8JsT4dPX6JGcwLwvT2LqsFzu/Mdq/mBTXBhj2tDpI5VFpL+ILBSRtSKyxuvKiohkicgbIrLRe84MMrbubdjZcMPrMPEGiEuEZ78Eny0lOSGO/7t2IheP7cv9r6znpy+vsyU4jTEtCrYNYSpwxEC0YAaniUgfoI+qrhCRNGA5rnH6OuCAqt4vIt8DMlX1zrbu1e3bEFpTeQAeOxtKdrj1FU79Kn6Fe15Yw1+Wbufyk/L42RdGExcb7O8BY0y0C2UbwsOquv3wRzDBqOpuVV3hbZcB64B+wEzgSe+yJ3FJwrRHchZ86R+Qd7Ib2Tz3TGKri7hv5ii+cdYwnluezy3zVtiIZmPMIcLahiAig4DxwPtAL1Xd7Z3aA/Rq5TU3icgyEVm2b1/LE74ZIGswzPk3nP8LKFgLC25GgG+dM5x7Lj6BN9YW8NV5K6itt7UUjDFOe9oQloZitlMRSQX+AdyuqqXNz3mlkRbrslR1rqpOVNWJubnWv75NMbEw6SY45z7Y+Br897cAXDdlMP8760T+s34vtzy9nLLqujAHaoyJBMEOTDs3FG8qIvG4ZDBPVf/pHS4QkT6quttrZ9gbivcywKSvwGeL4c0fw961cO5P+dKpA1Fcu8Jljy7h8esmkpeZHO5IjTFhFFAJQUTugKbG41MOaz/4SjBv6DVOPw6sU9VfNzv1AjDH254DPB/MfU0bYmLhsifcFBdr/gXzr4b6Gq45dSBPXn8Ku0qqmPXwYpZvLwp3pMaYMAq0yuiqZtt3HXbuvCDfcwpwDTBDRFZ5jwuA+4FzRGQjcLa3b0IlNh7Ovx8u+QPsWApPXwoVhZw+LId/3nIaSQkxXDV3Ca+v2RPuSI0xYRJoQpBWtlvab5OqvqeqoqpjVHWc93hZVQtV9SxVHaaqZ6vqgWDuawJ04hfc2s3b3oXHz4HtixnWK40XvzaVUX0zuPnp5Ty8cJNNn21MNxRoQtBWtlvaN5Fu8q3whf8Dfx08cQEs/BkZyfHM+/IkLhrTl1+8toEbn/yQkiprbDamOwk0IYwVkVIRKQPGeNuN+6M7MD7TUcZcATctghHnw9v3w+q/k+KL47dXjeP/zTqR9zbt59JHF7Nhj63TbEx3EVBCUNVYVU1X1TRVjfO2G/fjOzpI00FSsuGKp2DAZDeFdsEaRIRrTh3IY3NOprSqjsseXczLq3cf/V7GmKhncxd0d7HxcPmfwZcGf70Snv8abFnEmcNz+detUxjSM5WvzlvBd5/7iPKa+nBHa4zpQJYQDKT1hquecWsqrPwLPDUT3vgxfXsk8febJ/P1GUP5x4p8LnzoXVbtKA53tMaYDmIJwTh5J8FtH8F3N8Poy+G/D0L+cuJjY/j250Yw/6bJ1PuVSx9dzO//sxG/9UIypsuxhGAOEoGUHLduc0pPePk70OAmwDtlcBYv3zaVC0b34Zevf8rsuUvJL6oMc8DGmFAKKiGI8yURudvbHyAip3RMaCZsEtPhrLth1wp4cDTsXAFARlI8D101jt9cOZa1u0s5/7fv8sJHu8IcrDEmVIItITwCTAZme/tlwMMhjchEhvFfgksfd+0Kf5sDtRUAiAiXjM/j5W9MZVjPVL7xzEq+9ewqmyDPmC4g6NlOVfVWoBpAVYuAhJBHZcJPBEZfBrMegdJ8mDsd1r/UVIU0IDuZv31lMrefPYx/rdrJBQ+9y/LtNrjcmGgWbEKoE5FYvNHJIpIL2IT6XdngM+Dq56CqCOZ/Ed64u+lUXGwMt589nOdunowqXPHHpfz6jU+p89ufhDHRKNiE8BCwAOgpIj8B3gN+GvKoTGQZejZ8YyUMPw+W/B7+fBFUH1zC4qSBrsF55ti+PPTWRi555L98WmAjnI2JNsGuqZwHpABn4Sa1e0tV13VceG2zNZU7WdE2t8jO8ifdlBdX/AViDv1N8eonu/nBgk8oq67ntrOH8ZUzhtjazcZEkLbWVA44IXg3Wq2qETN3kSWEMFnyCLx2Fxz/eVelNO5qSDi4uE5heQ13P7+Gl1bvZnivVH588SimDM0JY8DGmEZtJYSwrqlsotSpt7gksO4FN1bhVyNh6ztNp7NTfTx89QT+eM1JVNX5ufqx9/nKX5bxWaGNWzAmkgVbQlgPDAW2AxW4aiNV1TEdE17brIQQRqpQvtetq7DwJ646acYP4aTrITmr6bLqOj+Pv7eVhxduot6vfHnqYG6dPpQUX7CrtxpjQiGUVUYDWzruLaXZ6SwhRIjKA/DMbLcSW3o/t9bCoCmHXLKnpJqfv7qef67cSc80H9+/4HhmjuuLa5oyxnSWkFUZeV/8pUAvYGCzh+nOkrPg+lfg2hfc/nNzoHDzIZf0zkjk11eO459fPY0+GYnc/uwqrpy7lNX5JWEI2BjTkmBLCF8GbsP1NloFnAosUdUZHRLdUVgJIQLt+QSevNgliSuegl6jjrikoUGZ/+EOfvHaeooq65gxsidfnzGU8QMywxCwMd1LKBuVbwNOBrar6nRgPFB8bOGZLqX3iXDl01C6C/4wFV69C+prD7kkJkb44qQBvH3HdL577ghWfFbEJY8s5prH3+fDbTba2ZhwCTYhVKtqNYCI+FR1PTAi9GGZqDZoCnxtGZzweVj6CDx7NfiPXFwnPTGeW6cP5b93zuCu80eybncpl/9hCVfNXcLizfsJpvRqjDl2wVYZLQCuB24HZgBFQLyqXtAh0R2FVRlFgQ8fh5e+Bbkj4bI/tViF1Kiq1s9fP/iMP769mb1lNUwcmMk3zhrG1GE51vhsTIiErJfRYTc9E8gAXlHVsEx1aQkhSvznf+GdX0DWcXDrBxDbdpfT6jo/f1u2g0cXbWZ3STWDspP52oxhXDy2D7642E4K2piuKZTdTu9u6biq3tfO2I6JJYQosu7f8OyXoN9EuGqeW7bzKGrq/Ty/ahdPLdnGJztLyU5J4MIxfbh+ymAG56R0QtDGdD2hTAjfbrabCFwErFPVG44txPaxhBBFVOHDx+CVO9xqbOOvhjPvhDjfUV/a0KC8u2k/Ty/dzsL1e1Fg+ohcrp8ymNOOy7bqJGOC0CFVRt6NfcBrqjqt3Tc5BpYQotCaBW6CvF0rXbtC79Ew5XbXOykAO4ur+MuS7Ty3bAeFFbUMyUlh1vh+zBrXjwHZyUe/gTHdXEcmhEzgQ1Ud2u6bHANLCFFs3Yvw+g/clBcSAyf/D5z/gFuYJwDVdX7+/dEu/r48n/e3uq6qEwb0YNb4flw4ug/ZqUcveRjTHYV0tlO8xXGAWCAXuE9Vf3/MUbaDJYQoV1cFny2Bj/8GHz3jSgrn3Bv0bXYWV/HCql08v2on6/eUERcjnDE8l1nj+3HBib1t+m1jmumouYzqgQJVPbKDeSexhNBFNDTAi7fBiqdg/DUwaKpbvjMm+B5F63aX8q9VO3lh1S52l1STkRTP6H4ZXDt5ICf0TScv06qVTPfWYVVG4WYJoQtp8LsJ8ja+5vaHn+/GLSS07wu8oUF5fW0Bizbs5b1N+8kvqgLgcyf04qKxfTnn+F4kJVgXVtP9hLKE8K22zqvqr4OM7ZhYQuhiGvxwYAus/ju8fT9kDICvvH3IdNrtUedv4N2N+3hr3V5eW1PA/vIakuJjOWVwFlOH5TB1WC4jeqeF6EMYE9lCmRD+ipvLyJvWkouBD4CNAKoafAXwMbCE0EWpwif/gH/cCEOmQY+B8Ln/B4kZx3zrhgZl6dZCXl9TwLsb97F5XwUAI3qlcdrQbE47LoeTB2XSIznhmN/LmEgUyoTwDnChqpZ5+2nAS6p6RkgiDZIlhC7ur1fBp6+47UFT3eypx1haONyu4ipe+WQPizbsZcnmQuoblIS4GE7sm87pw3I5fWgOJw3MJDbGxjqYriGUCWEDMEZVa7x9H/CxqoZlgjtLCF1cgx+Kt8OWRfDiN92xU26CM+6A1NyQv11lbT3vbdzP+1sP8P7WQtbuKqVBIdUXx4SBmYzLyyAjOYELRvemT0ZSyN/fmM4QyoTwA+AKYAFu+cxZwHxV/VkQ9/gTboTzXlU90TuWBTwLDAK2AVeoatHR7mUJoRtZ8y947zewe5XbHzIdrvpruxudA1FeU8/C9XtZuqWQZduK2Li3jAbvf5fGKqbxAzKZNDiLnmk+GzFtokJIexmJyARgKm48wruqujLI158BlANPNUsIPwcOqOr9IvI9IFNV7zzavSwhdEObF7p1nN/9ldufdpcrMcR0/FiDytp6tu2vZNGne1m8qZAPtx2gpr4BgLzMJCYMyOT4Pukc3yeNUX0zyE2zwXEm8hxzQhCRk4EdqrrH258DXIr7NX+Pqga1qomIDAJebJYQNgDTVHW3iPQBFgVSDWUJoRt75U7XG6lyPww7F0ZdAgNPgx4DAh7tfKxq6xv4OL+Y1TtLWLK5kDW7StlZXNV0vle6j9H9MjiuZyojeqUxvFcaI3qnEW8D5UwYhSIhrADOVtUD3i/8+cDXgXHA8ap6WZABDeLQhFCsqj28bQGKGvdbeO1NwE0AAwYMOGn79u3BvLXpSlThnV/Cop+B+t2x9Dw4+8eQkOKqlTqwSqklJZV1rNtTyppdpXyys4RPdpawZX8Ffq+uKT0xjmGNyaFXKsN7pzGiV5pNtWE6TSgSwkeqOtbbfhjYp6r3ePurVHVckAENopWE4O0XqepRF9i1EoIBoLYSCtbA5rfgvw9BnetKSs5wmHAtTLzBJYgwaWhQ1u4uZdPect7feoDNe8vZUFBGSdXBZURyUhMY1jONfplJDO+VyoCsFEb1TadvjyTr4WRCKhQJ4RNgnKrWi8h64CZVfafxXOMXexABDcKqjExH2PcpbHsHSnfD+pdg37qD50ZcCJf8ARLTwxefR1XZV1bDhoIyNuwp49OCMj4tKGd3SRUFpTVN1yXExTA4O4Uhud4jJ5Xs1ASyUhIY2jOV5IS2Fxsy5nBtJYRA/5qeAd4Wkf1AFfCud+OhQEkIYnwBmAPc7z0/H4J7mu4od7h7AJz1I9cI/d5vYOvbsOEleGomnPczGHBqWMMUEXqmJ9IzPZGpww7tQruvrIadxVWs213K1v0VbNlXzoY9ZbyxtoD6Bm12D9eYnZvqIyfVx5i8DPIyk+mdkUi/Hkn0yUi0if1MUALuZSQipwJ9gNdVtcI7NhxIVdUVAb+hyDPANCAHKAB+DPwL+BswANiO63Z61IZqKyGYgBVtd1VKix6A8gIYfi6Mvtw1Qued3GkN0ceizt/AjgOV7C+v5UBFLZ8WlLFpbzkHKmrZUVTJ9sLKQ66PjRHXZtEzjQHZydTWNzC6XwaDc1IYlJNMWmI8WSkJ1sjdzdjkdsY0qimHt+6DlX+BOu8LdOg5MPYqOGHWUdd7jmSVtfXsLqlmd3E1O4sr2XGgij2l1WzaW86ekmrqG5T95TWHvCY5IZaeaT76ZSaRleIjPTGOkX3SyU5JID0xnsG5KaQlxpGeGB+mT2VCzRKCMYer2A/PXgP+WijcBNXFkD3UTb897BzIHgZxXWs+I1WlqLKO7YUVfHagkrLqej4tKONARS35RVWUVNWxv7yGsuojZ7TPSU0gJ9VHduNzio+cNLd98JyP7JQEEuNtFtlIZgnBmLY0+N0CPf/5CZTtOnh8zFUw/kuQ3heSsyGpR9hC7CwNDUpBWTV7Sqopra5ny75yKmv95BdVUVhew/7yGvaX11JYXkNFrb/Fe6T54shJc8khJ9VHckIsKb44BmYnk5XiGsQT42PZsq+Csf0zOL53OjHWk6rTWEIwJhD+etj/qZs7aekjULLj4Lm0vvCVdzpkDqVoVVXr9xLEwSTRuN14vLC8lqLKOkqr6qj1N7R4n4TYGNKT4qmp99M/M5n+WUlkJMWTmZxAbpqPFJ+rsspIiicuVhiUnUJqYhwNqlTX+clJ8SGCTR0SoFDOZeTDjVAeRLMeSqp63zHG2C6WEEyH8dfDmgWwdy0UboQNr0LmIOgzBjeNl0LOCDhuBvQ7qVOmzohmqkppdT1FFbUUVtRSUlVLdoqPtbtL2ba/guLKOhLiYvjsQCU7iiopraqnpKqWOv/Rv59cQokjJ9VHWmIcGUkJDMlNIS5GSPHFkRQfS1JCLEnxsQzOScGvSlyMkJvmIzM5gRgR/A3abRZMCmVCeBXXzXQ50FReVNVfHWuQ7WEJwXSadS/C4odgxwcQGw/JOYdWLw2ZDj1PgNwRkDnQbaf2DF+8XUC9v4GKWj+VtfWUVNVRUllHdX0DnxVWUFXnRxD2lddQXFmLvwHKqusora5jb1kN+QeqaFA9pJvu0aQlxrnG9KR4YmOEOn8DPZISUJRUXxyj+mYQHxtDckIsdf4G8jKTqKlvIMO7vrFdJT4mhqSEWHxxMYgI1XV+EmJjIqZaLJQJIehBaB3JEoLpdCX5gEBGPyjf58Y2fPg47Pn4yGsnXAuVB9zjxC+47q0b34Ds49zCPyFe28EcqarWT1l1HVV1fqrrGtheWEF8XAz1fjcwcH95DQcqaslMTqCo0nXnLa2uw9+gxIhQXFnLvrIaauobKKyoDeq9k+JjUZTqugZSfXHUNzQwICuZOr8iwKCcFHJTffjiY4gRIUaEVF8sFbV+Urx2lxRfHHmZScSIEB8bQ4MqifExJMTGMjqvfQtGhTIhzAV+p6qr2xVJiFlCMBGjusR98Rd8Ajveh5XzoOqAm1upNP/I62N9MHCyq3aKiYO+41zvpqRM2PoOlBXAkDMPljL8dVC0zVVbxcZDfY2byyk+sRM/ZPdWXlOPv0Gp8zcQK8LmfeUkJcRSVFFHdZ2fkqo6Kmrrqfcr1fV+9pfVEhsDdX7X1pEYH8vO4ioOVNTSoEpJpbu+pr6BhgbF36CtNtQfLjkhlrX3ndeuzxHKhLAWGApsBWrwKlNVdUy7IjtGlhBMxGrww66V0He8+zJ/91dQugtOmOkm3Fv1DGx/DyoKobbs4OvS+h6siopNgOM/D/s3wB7vN1iPAZDY42CJZMK1rvqqYA2c+1M3Z5MvzY2xiE92zwkpblsbICbWJZu0Xp36z2ECU1PvJy4mhtr6Boqraqms9XOgohYBav0NqLoBigDTRrSvSjKUCWFgS8dVNSxTjlpCMF2Cvw42vOy+1POXuYbrkRfDqnnw0XxoqAd/DQw+A/Z8AlVFkNYbso5zSeVoJNab3E9csijNh7Q+MHa2q8rSBij+DGrK3PiLhGRI73ewm21thWs7GXS6K50AbHnbNbhPmNPpM8p2e3XVrlTZzkGUoV4gJxMYBjSVVRsnuutslhBMt6PqSh+NXwaf/BO2/xeGnw8V+6C+CvZtgPUvu7aKknzof4o7VrDGtVv0n+Sqn3a1NeOMQK9RbrBe4WYo8EooWce55wOb3bMv3fWy6jUKeo92pSB/HfQ6AUZeBAe2wNs/d++VlOXWrOg1CuqqYOPr7rOMvhROuMT11Nr3qftsWUPc/Xd/7KYcmXL7kdOL+Ouh5LOD1zZXXeLuHUg7TfEOV3JKbVZqkhj3OapLDnY1rquGON/BOPauc5+j34Sjv0dLqopd0i3f6xJ1vLcsa32NS8Kx8bDyaRh3tZuQceXTroS46U1Y+zx85d12TdQYyhLCl4HbgDxgFXAqsERVZwQdVQhYQjAmCA0NeB323fbWt2HZ4+6LddAU2LXKlUSyh0JcoisV7F3nRmwn9nBJJakHlO12JYlRs9zkgbXlsG996+8rsa7n1YEtrV8TlwiZgw/OTjthjhtF/tEzB6/pPwkSM+DMO2HTW7D8z656bfw1MGiq+wLd8b5LNKW7XdxDpkPvE6H/qe5Ld/9GSO/jrskZfrDqLjHDlYr2bXDJIbEHVOx179trtEsKW952CzHN/D2s+iu89C13Pj4ZTv2qK4V9+LgrSfUd5xJMak/45B+uTSkxHWLi4bSvuSnbF/4Ejr/IdTSor4YeA92ki9uXuFJgUqZLeD0Guv2a0oP/FsfNgGsWBPkH4P3nCGFCWA2cDCxV1XEiMhL4qap+oV2RHSNLCMZ0ovpa9yVbUei+rJqPvagodKvXJaS6X9fPf9U1nGcNgXFfdF/KBWtdQ/u6F90X7P6NkDMM4pLgjbuhoe7I90zr4xJQMBLSYOgMN6Fh4xrcgUrtBYPPdImw4Gh9Z7zxKIe8vjeU7wnuPbOGuH+3gjXu37Vy/5HXpOS6hHlgs/v3umbBwVl9gxSK6a8bVatqtYggIj5VXS8iR123wBjTBTTO7ZSSfeS5lOxDj7f067XXCe550OlHnpt4vWvLUIXKQtegnpLjSheoSwqNX7ab/+N+8WcNcb/m6yrdfFQN9S7JDD3bvVbVtY001MOWha4aZuDprlSR1hfW/NO1oaTnwfuPum7BIy908dTXwM7l0HuMi2fJ71211da3Xcnk+Ivh1FvdaPb6Ghejv86VOv77W1j3vJv6pKbUVbmNnQ2b3oCJN7r4a8pg/NWu9HDyl111UYPffR5wpbZ96914lqKtkNHf/fs3NHToIMhgSwgLgOuB24EZQBEQr6oXdEh0R2ElBGOMCU7ISgiqeom3eY+ILAQygFePMT5jjDERIKiyhzhfEpG7VfVtXMPyuI4IzBhjTOcKtjLqEWAyMNvbLwMeDmlExhhjwiLYRuVJqjpBRFYCqGqRiHStVUSMMaabCraEUCfS2OwPIpILtDzJuTHGmKgSbEJ4CFgA9BKRnwDvAT8LeVTGGGM6XbC9jOaJyHLgLO/QTFVtY4iiMcaYaBFQQhCRFw4/5D2fKyKo6udDG5YxxpjOFmgJYTKwA3gGeJ+DCcEYY0wXEWhC6A2cg+tu+kXgJeAZVV3TUYEZY4zpXAE1KquqX1VfVdU5uBlONwGLRORrHRqdMcaYThNwo7KI+IALcaWEQRzscWSMMaYLCLRR+SngROBl4F5V/aRDozLGGNPpAi0hfAmowC2O8w05uHJR45rKwS/bY4wxJqIElBBUteMm4DbGGBMR7IveGGMMYAnBGGOMxxKCMcYYwBKCMcYYjyUEY4wxgCUEY4wxnohKCCJynohsEJFNIvK9cMdjjDHdScQkBG8ltoeB84ETgNkickJ4ozLGmO4jYhICcAqwSVW3qGotMB+YGeaYjDGm2whqxbQO1g+35kKjfGDS4ReJyE3ATd5uuYhsaOf75QD72/nacIvm2MHiD6dojh2iO/5IiX1gayciKSEERFXnAnOP9T4iskxVJ4YgpE4XzbGDxR9O0Rw7RHf80RB7JFUZ7QT6N9vP844ZY4zpBJGUED4EhonIYBFJAK4CDl/L2RhjTAeJmCojVa33VmB7DYgF/tTBS3Qec7VTGEVz7GDxh1M0xw7RHX/Exy6qGu4YjDHGRIBIqjIyxhgTRpYQjDHGAN0wIUTD9Bgi8icR2SsinzQ7liUib4jIRu850zsuIvKQ93k+FpEJ4YscRKS/iCwUkbUiskZEbouy+BNF5AMR+ciL/17v+GARed+L81mv4wMi4vP2N3nnB4Uzfi+mWBFZKSIvevvRFPs2EVktIqtEZJl3LFr+dnqIyN9FZL2IrBORydESe6NulRCiaHqMPwPnHXbse8BbqjoMeMvbB/dZhnmPm4BHOynG1tQD31bVE4BTgVu9f+Noib8GmKGqY4FxwHkicirwAPAbVR0KFAE3etffCBR5x3/jXRdutwHrmu1HU+wA01V1XLM++9Hyt/Nb4FVVHQmMxf03iJbYHVXtNg9gMvBas/27gLvCHVcrsQ4CPmm2vwHo4233ATZ4238EZrd0XSQ8gOeBc6IxfiAZWIEbMb8fiDv87wjXK26ytx3nXSdhjDkP98UzA3gRkGiJ3YtjG5Bz2LGI/9sBMoCth//7RUPszR/dqoRAy9Nj9AtTLMHqpaq7ve09QC9vO2I/k1cFMR54nyiK36tyWQXsBd4ANgPFqlrvXdI8xqb4vfMlQHanBnyoB4E7gAZvP5voiR1AgddFZLk3TQ1Ex9/OYGAf8IRXXfeYiKQQHbE36W4JoUtQ95MiovsLi0gq8A/gdlUtbX4u0uNXVb+qjsP92j4FGBneiAIjIhcBe1V1ebhjOQanq+oEXJXKrSJyRvOTEfy3EwdMAB5V1fFABQerh4CIjr1Jd0sI0Tw9RoGI9AHwnvd6xyPuM4lIPC4ZzFPVf3qHoyb+RqpaDCzEVbP0EJHGgZzNY2yK3zufARR2bqRNpgCfF5FtuNmCZ+DqtaMhdgBUdaf3vBdYgEvI0fC3kw/kq+r73v7fcQkiGmJv0t0SQjRPj/ECMMfbnoOrm288fq3Xa+FUoKRZEbXTiYgAjwPrVPXXzU5FS/y5ItLD207CtX+swyWGy7zLDo+/8XNdBvzH+yXY6VT1LlXNU9VBuL/t/6jq1URB7AAikiIiaY3bwOeAT4iCvx1V3QPsEJER3qGzgLVEQeyHCHcjRmc/gAuAT3H1wj8IdzytxPgMsBuow/3yuBFXt/sWsBF4E8jyrhVcz6nNwGpgYphjPx1XLP4YWOU9Loii+McAK734PwHu9o4PAT4ANgHPAT7veKK3v8k7PyTcfz9eXNOAF6Mpdi/Oj7zHmsb/P6Pob2ccsMz72/kXkBktsTc+bOoKY4wxQPerMjLGGNMKSwjGGGMASwjGGGM8lhCMMcYAlhCMMcZ4LCEY0wYR8XszbzY+QjZDrogMkmYz2hoTbhGzhKYxEapK3TQWxnR5VkIwph28eft/7s3d/4GIDPWODxKR/3hz3L8lIgO8471EZIG4dRY+EpHTvFvFisj/iVt74XVvdLQxYWEJwZi2JR1WZXRls3Mlqjoa+D1ullGA3wFPquoYYB7wkHf8IeBtdessTMCNxAU3H/7DqjoKKAYu7dBPY0wbbKSyMW0QkXJVTW3h+DbcQjpbvMn89qhqtojsx81rX+cd362qOSKyD8hT1Zpm9xgEvKFu8RRE5E4gXlX/txM+mjFHsBKCMe2nrWwHo6bZth9r1zNhZAnBmPa7stnzEm97MW6mUYCrgXe97beAW6BpAZ6MzgrSmEDZrxFj2pbkrZ7W6FVVbex6mikiH+N+5c/2jn0dt2rWd3EraF3vHb8NmCsiN+JKArfgZrQ1JmJYG4Ix7eC1IUxU1f3hjsWYULEqI2OMMYCVEIwxxnishGCMMQawhGCMMcZjCcEYYwxgCcEYY4zHEoIxxhgA/j9Wa9Tz7M3dHQAAAABJRU5ErkJggg==\n", 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" ] @@ -283,8 +294,8 @@ " plt.figure()\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Mean Square Error [Thousand Dollars$^2$]')\n", - " plt.plot(hist['epoch'], hist['mean_squared_error'], label='Train Error')\n", - " plt.plot(hist['epoch'], hist['val_mean_squared_error'], label = 'Val Error')\n", + " plt.plot(hist['epoch'], hist['mse'], label='Train Error')\n", + " plt.plot(hist['epoch'], hist['val_mse'], label = 'Val Error')\n", " plt.legend()\n", " plt.ylim([0,50])\n", "\n", @@ -303,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": { "colab": {}, "colab_type": "code", @@ -314,8 +325,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "102/102 [==============================] - 0s 44us/step\n", - "Root Mean Square Error on test set: 4.244\n" + "4/4 [==============================] - 0s 1ms/step - loss: 16.9950 - mae: 2.7578 - mse: 16.9950\n", + "Root Mean Square Error on test set: 4.122\n" ] } ], @@ -347,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -372,13 +383,22 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 26, "metadata": { "colab": {}, "colab_type": "code", "id": "" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n", + "11493376/11490434 [==============================] - 0s 0us/step\n" + ] + } + ], "source": [ "(train_images, train_labels), (test_images, test_labels) = keras.datasets.mnist.load_data()\n", "\n", @@ -399,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 27, "metadata": { "colab": {}, "colab_type": "code", @@ -428,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 28, "metadata": { "colab": {}, "colab_type": "code", @@ -437,7 +457,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -471,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 29, "metadata": { "colab": {}, "colab_type": "code", @@ -514,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 33, "metadata": { "colab": {}, "colab_type": "code", @@ -522,7 +542,7 @@ }, "outputs": [], "source": [ - "model.compile(optimizer=tf.train.AdamOptimizer(), \n", + "model.compile(optimizer=tf.keras.optimizers.Adam(), \n", " loss='sparse_categorical_crossentropy',\n", " metrics=['accuracy'])" ] @@ -547,7 +567,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 34, "metadata": { "colab": {}, "colab_type": "code", @@ -559,15 +579,15 @@ "output_type": "stream", "text": [ "Epoch 1/5\n", - "60000/60000 [==============================] - 7s 121us/step - loss: 0.1953 - acc: 0.9410\n", + "1875/1875 [==============================] - 65s 34ms/step - loss: 0.3789 - accuracy: 0.8789\n", "Epoch 2/5\n", - "60000/60000 [==============================] - 6s 100us/step - loss: 0.0842 - acc: 0.9753\n", + "1875/1875 [==============================] - 66s 35ms/step - loss: 0.0905 - accuracy: 0.9731\n", "Epoch 3/5\n", - "60000/60000 [==============================] - 6s 96us/step - loss: 0.0642 - acc: 0.9810\n", + "1875/1875 [==============================] - 66s 35ms/step - loss: 0.0599 - accuracy: 0.9820\n", "Epoch 4/5\n", - "60000/60000 [==============================] - 6s 94us/step - loss: 0.0526 - acc: 0.9835\n", + "1875/1875 [==============================] - 65s 35ms/step - loss: 0.0488 - accuracy: 0.9849\n", "Epoch 5/5\n", - "60000/60000 [==============================] - 6s 94us/step - loss: 0.0443 - acc: 0.9861\n" + "1875/1875 [==============================] - 65s 35ms/step - loss: 0.0389 - accuracy: 0.9879\n" ] } ], @@ -599,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 35, "metadata": { "colab": {}, "colab_type": "code", @@ -611,8 +631,8 @@ "output_type": "stream", "text": [ "(10000, 28, 28, 1)\n", - "10000/10000 [==============================] - 1s 50us/step\n", - "Test accuracy: 0.9913\n" + "313/313 [==============================] - 2s 8ms/step - loss: 0.0298 - accuracy: 0.9916\n", + "Test accuracy: 0.991599977016449\n" ] } ], @@ -649,7 +669,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", @@ -658,7 +678,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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