From b86066a47b042c48ca92aa53bf1471a0fa37e138 Mon Sep 17 00:00:00 2001 From: Insan Cahya Setia <33500914+insancs@users.noreply.github.com> Date: Thu, 14 Oct 2021 13:24:57 +0700 Subject: [PATCH] Dibuat menggunakan Colaboratory --- rockpaperscissors.ipynb | 814 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 814 insertions(+) create mode 100644 rockpaperscissors.ipynb diff --git a/rockpaperscissors.ipynb b/rockpaperscissors.ipynb new file mode 100644 index 0000000..4259e88 --- /dev/null +++ b/rockpaperscissors.ipynb @@ -0,0 +1,814 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "rockpaperscissors.ipynb", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true, + "authorship_tag": "ABX9TyNmBT1jsuJFDOjqkjufXanO", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dNhxXQBec7ru" + }, + "source": [ + "# Import Libraries" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Oe3R3arNwxdG", + "outputId": "0daf92b1-65b3-4ece-c653-4c270cc2633a" + }, + "source": [ + "# import libraries\n", + "import numpy as np\n", + "import zipfile\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "from google.colab import files\n", + "from keras.preprocessing import image\n", + "import matplotlib.image as mpimg\n", + "%matplotlib inline\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.callbacks import Callback\n", + "# Check tensorflow version\n", + "print(tf.__version__)" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2.6.0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5x3vRX7PF8zF" + }, + "source": [ + "# Import Dataset" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "S1961wS8yUwA", + "outputId": "e18e95ed-ee8c-4308-ae29-c854194d9306" + }, + "source": [ + "# import datasets\n", + "!wget --no-check-certificate \\\n", + " https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps.zip \\\n", + " -O /tmp/rps.zip\n", + "\n", + "!wget --no-check-certificate \\\n", + " https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps-test-set.zip \\\n", + " -O /tmp/rps-test-set.zip" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2021-10-14 05:40:29-- https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps.zip\n", + "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.142.128, 74.125.195.128, 173.194.202.128, ...\n", + "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.142.128|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 200682221 (191M) [application/zip]\n", + "Saving to: ‘/tmp/rps.zip’\n", + "\n", + "/tmp/rps.zip 100%[===================>] 191.38M 138MB/s in 1.4s \n", + "\n", + "2021-10-14 05:40:30 (138 MB/s) - ‘/tmp/rps.zip’ saved [200682221/200682221]\n", + "\n", + "--2021-10-14 05:40:31-- https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps-test-set.zip\n", + "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.142.128, 74.125.195.128, 142.250.107.128, ...\n", + "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.142.128|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 29516758 (28M) [application/zip]\n", + "Saving to: ‘/tmp/rps-test-set.zip’\n", + "\n", + "/tmp/rps-test-set.z 100%[===================>] 28.15M 106MB/s in 0.3s \n", + "\n", + "2021-10-14 05:40:31 (106 MB/s) - ‘/tmp/rps-test-set.zip’ saved [29516758/29516758]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Fw2uaJsnGHoY" + }, + "source": [ + "# Extract file" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "edvJ0tulyU0T" + }, + "source": [ + "# Extract zip file\n", + "local_zip = '/tmp/rps.zip'\n", + "zip_ref = zipfile.ZipFile(local_zip, 'r')\n", + "zip_ref.extractall('/tmp/')\n", + "zip_ref.close()\n", + "\n", + "local_zip = '/tmp/rps-test-set.zip'\n", + "zip_ref = zipfile.ZipFile(local_zip, 'r')\n", + "zip_ref.extractall('/tmp/')\n", + "zip_ref.close()" + ], + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Emw41CFaSzcV", + "outputId": "2b422623-b3d8-47dc-a0d8-8096b596781c" + }, + "source": [ + "# Check rps directory\n", + "os.listdir('/tmp/rps')" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['rock', 'scissors', 'paper']" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8abO6LM-KoNX", + "outputId": "6f5163bf-a296-4c99-bdfc-f9e064a5815e" + }, + "source": [ + "# Check rps-test-set direcroty\n", + "os.listdir('/tmp/rps-test-set')" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['rock', 'scissors', 'paper']" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wDg-zKEHGUIX" + }, + "source": [ + "# Image Augmentation" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yCpuSkhGTiAf" + }, + "source": [ + "# image augmentation\n", + "train_datagen = ImageDataGenerator(\n", + " rescale = 1./255,\n", + "\t rotation_range=40,\n", + " width_shift_range=0.2,\n", + " height_shift_range=0.2,\n", + " shear_range=0.2,\n", + " zoom_range=0.2,\n", + " horizontal_flip=True,\n", + " fill_mode='nearest',\n", + ")\n", + "\n", + "validation_datagen = ImageDataGenerator(\n", + " rescale = 1./255,\n", + ")" + ], + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_tELOhr8Urlq", + "outputId": "13c968e3-a512-45e4-d62e-65de954a1b99" + }, + "source": [ + "TRAINING_DIR = \"/tmp/rps/\"\n", + "train_generator = train_datagen.flow_from_directory(\n", + " TRAINING_DIR,\n", + " target_size = (150, 150),\n", + " batch_size=32,\n", + " class_mode='categorical',\n", + ")\n", + "\n", + "VALIDATION_DIR = \"/tmp/rps-test-set/\"\n", + "validation_generator = validation_datagen.flow_from_directory(\n", + " VALIDATION_DIR,\n", + " target_size = (150, 150),\n", + " batch_size = 32,\n", + " class_mode = 'categorical',\n", + ")" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found 2520 images belonging to 3 classes.\n", + "Found 372 images belonging to 3 classes.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2fmuiaNEGkny" + }, + "source": [ + "# Convolution Neural Network" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Lk1mA5nYZ7ZI", + "outputId": "d223c3d0-be41-43ab-b050-5331cb905aac" + }, + "source": [ + "model = tf.keras.models.Sequential([\n", + " # Note the input shape is the desired size of the image 150x150 with 3 bytes color\n", + " # This is the first convolution\n", + " tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(150, 150, 3)),\n", + " tf.keras.layers.MaxPooling2D(2, 2),\n", + " # The second convolution\n", + " tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n", + " tf.keras.layers.MaxPooling2D(2,2),\n", + " # The third convolution\n", + " tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n", + " tf.keras.layers.MaxPooling2D(2,2),\n", + " # The fourth convolution\n", + " tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n", + " tf.keras.layers.MaxPooling2D(2,2),\n", + " # Flatten the results to feed into a DNN\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dropout(0.5),\n", + " # 512 neuron hidden layer\n", + " tf.keras.layers.Dense(512, activation='relu'),\n", + " tf.keras.layers.Dense(3, activation='softmax')\n", + "])\n", + "\n", + "# Tampilkan summary model\n", + "model.summary()" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "conv2d (Conv2D) (None, 148, 148, 64) 1792 \n", + "_________________________________________________________________\n", + "max_pooling2d (MaxPooling2D) (None, 74, 74, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_1 (Conv2D) (None, 72, 72, 64) 36928 \n", + "_________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2 (None, 36, 36, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 34, 34, 128) 73856 \n", + "_________________________________________________________________\n", + "max_pooling2d_2 (MaxPooling2 (None, 17, 17, 128) 0 \n", + "_________________________________________________________________\n", + "conv2d_3 (Conv2D) (None, 15, 15, 128) 147584 \n", + "_________________________________________________________________\n", + "max_pooling2d_3 (MaxPooling2 (None, 7, 7, 128) 0 \n", + "_________________________________________________________________\n", + "flatten (Flatten) (None, 6272) 0 \n", + "_________________________________________________________________\n", + "dropout (Dropout) (None, 6272) 0 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, 512) 3211776 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 3) 1539 \n", + "=================================================================\n", + "Total params: 3,473,475\n", + "Trainable params: 3,473,475\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "S54IrvPzZ7c6" + }, + "source": [ + "# Compile model\n", + "model.compile(loss = 'categorical_crossentropy', \n", + " optimizer='rmsprop',\n", + " metrics=['accuracy'])" + ], + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NjeNa_OO5zFe" + }, + "source": [ + "# Create Callbacks" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "r1IUTXpfDaWi" + }, + "source": [ + "# Buat callback kostumisasi\n", + "class CustomCallback(Callback):\n", + " def on_train_begin(self, logs=None):\n", + " print(\"Starting training\")\n", + "\n", + " def on_train_end(self, logs=None):\n", + " print(\"Training has been stopped\")\n", + " print('Accuracy = %2.2f%%' %(logs['accuracy']*100))\n", + "\n", + "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss', \n", + " factor=0.2,\n", + " patience=5, \n", + " min_lr=1.5e-5\n", + ")\n", + "\n", + "early_stopping = tf.keras.callbacks.EarlyStopping(\n", + " monitor=\"val_loss\",\n", + " min_delta=0,\n", + " patience=12,\n", + " verbose=0,\n", + " mode=\"auto\",\n", + " baseline=None,\n", + " restore_best_weights=True\n", + ")" + ], + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "o_WBeHNOdvM5", + "outputId": "dbc7044f-545c-4ec9-b0db-0aa15756c1ad" + }, + "source": [ + "history = model.fit(train_generator, \n", + " epochs=30,\n", + " steps_per_epoch=20,\n", + " validation_data = validation_generator,\n", + " verbose = 1,\n", + " validation_steps=3,\n", + " callbacks=[CustomCallback(), reduce_lr, early_stopping]\n", + " )" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Starting training\n", + "Epoch 1/30\n", + "20/20 [==============================] - 44s 2s/step - loss: 1.3408 - accuracy: 0.3719 - val_loss: 1.0886 - val_accuracy: 0.4271\n", + "Epoch 2/30\n", + "20/20 [==============================] - 43s 2s/step - loss: 1.1108 - accuracy: 0.3781 - val_loss: 1.1073 - val_accuracy: 0.3854\n", + "Epoch 3/30\n", + "20/20 [==============================] - 42s 2s/step - loss: 1.0841 - accuracy: 0.4430 - val_loss: 0.8570 - val_accuracy: 0.4896\n", + "Epoch 4/30\n", + "20/20 [==============================] - 42s 2s/step - loss: 1.1040 - accuracy: 0.5016 - val_loss: 1.1516 - val_accuracy: 0.3854\n", + "Epoch 5/30\n", + "20/20 [==============================] - 42s 2s/step - loss: 0.9057 - accuracy: 0.5491 - val_loss: 0.5212 - val_accuracy: 0.8750\n", + "Epoch 6/30\n", + "20/20 [==============================] - 42s 2s/step - loss: 0.8866 - accuracy: 0.5906 - val_loss: 0.4690 - val_accuracy: 0.8125\n", + "Epoch 7/30\n", + "20/20 [==============================] - 42s 2s/step - loss: 0.7362 - accuracy: 0.6500 - val_loss: 0.3730 - val_accuracy: 0.8333\n", + "Epoch 8/30\n", + "20/20 [==============================] - 42s 2s/step - loss: 0.6914 - accuracy: 0.6962 - val_loss: 0.4634 - val_accuracy: 0.9271\n", + "Epoch 9/30\n", + "20/20 [==============================] - 43s 2s/step - loss: 0.6160 - accuracy: 0.7312 - val_loss: 0.3328 - val_accuracy: 0.8438\n", + "Epoch 10/30\n", + "20/20 [==============================] - 43s 2s/step - loss: 0.5483 - accuracy: 0.7641 - val_loss: 0.2598 - val_accuracy: 0.8542\n", + "Epoch 11/30\n", + "20/20 [==============================] - 42s 2s/step - loss: 0.5606 - accuracy: 0.7859 - val_loss: 0.3083 - val_accuracy: 0.8229\n", + "Epoch 12/30\n", + "20/20 [==============================] - 43s 2s/step - loss: 0.4561 - accuracy: 0.8016 - val_loss: 0.1387 - val_accuracy: 0.9583\n", + "Epoch 13/30\n", + "20/20 [==============================] - 42s 2s/step - loss: 0.4297 - accuracy: 0.8391 - val_loss: 0.0563 - val_accuracy: 1.0000\n", + "Epoch 14/30\n", + "20/20 [==============================] - 42s 2s/step - loss: 0.3752 - accuracy: 0.8576 - val_loss: 0.5526 - val_accuracy: 0.7500\n", + "Epoch 15/30\n", + "20/20 [==============================] - 43s 2s/step - loss: 0.4306 - accuracy: 0.8188 - val_loss: 0.2030 - val_accuracy: 0.8958\n", + "Epoch 16/30\n", + "20/20 [==============================] - 42s 2s/step - loss: 0.3405 - accuracy: 0.8562 - val_loss: 0.2023 - val_accuracy: 0.9271\n", + "Epoch 17/30\n", + "20/20 [==============================] - 42s 2s/step - loss: 0.3253 - accuracy: 0.8672 - val_loss: 0.3185 - val_accuracy: 0.7812\n", + "Epoch 18/30\n", + "20/20 [==============================] - 42s 2s/step - loss: 0.2786 - accuracy: 0.9098 - val_loss: 0.1881 - val_accuracy: 0.9167\n", + "Epoch 19/30\n", + "20/20 [==============================] - 43s 2s/step - loss: 0.1925 - accuracy: 0.9406 - val_loss: 0.0537 - val_accuracy: 0.9792\n", + "Epoch 20/30\n", + "20/20 [==============================] - 45s 2s/step - loss: 0.1572 - accuracy: 0.9438 - val_loss: 0.0845 - val_accuracy: 0.9688\n", + "Epoch 21/30\n", + "20/20 [==============================] - 43s 2s/step - loss: 0.1719 - accuracy: 0.9399 - val_loss: 0.0461 - val_accuracy: 0.9896\n", + "Epoch 22/30\n", + "20/20 [==============================] - 43s 2s/step - loss: 0.1390 - accuracy: 0.9446 - val_loss: 0.0594 - val_accuracy: 0.9688\n", + "Epoch 23/30\n", + "20/20 [==============================] - 43s 2s/step - loss: 0.1362 - accuracy: 0.9438 - val_loss: 0.0515 - val_accuracy: 0.9688\n", + "Epoch 24/30\n", + "20/20 [==============================] - 43s 2s/step - loss: 0.1341 - accuracy: 0.9469 - val_loss: 0.0341 - val_accuracy: 0.9896\n", + "Epoch 25/30\n", + "20/20 [==============================] - 43s 2s/step - loss: 0.1606 - accuracy: 0.9430 - val_loss: 0.0154 - val_accuracy: 1.0000\n", + "Epoch 26/30\n", + "20/20 [==============================] - 44s 2s/step - loss: 0.1202 - accuracy: 0.9547 - val_loss: 0.0916 - val_accuracy: 0.9479\n", + "Epoch 27/30\n", + "20/20 [==============================] - 43s 2s/step - loss: 0.1355 - accuracy: 0.9641 - val_loss: 0.0580 - val_accuracy: 0.9792\n", + "Epoch 28/30\n", + "20/20 [==============================] - 44s 2s/step - loss: 0.1128 - accuracy: 0.9547 - val_loss: 0.0447 - val_accuracy: 0.9792\n", + "Epoch 29/30\n", + "20/20 [==============================] - 44s 2s/step - loss: 0.0741 - accuracy: 0.9781 - val_loss: 0.0274 - val_accuracy: 1.0000\n", + "Epoch 30/30\n", + "20/20 [==============================] - 43s 2s/step - loss: 0.1203 - accuracy: 0.9604 - val_loss: 0.0556 - val_accuracy: 0.9688\n", + "Training has been stopped\n", + "Accuracy = 96.04%\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lOanbS5dG7aI" + }, + "source": [ + "# Model Evaluate" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IZhyaw4-34l8", + "outputId": "8afee76a-d3ce-40eb-906a-41f64a4c4109" + }, + "source": [ + "# Evaluasi model pada training data\n", + "print(\"Evaluasi train data\")\n", + "results = model.evaluate(train_generator, batch_size=32)\n", + "print('Loss: {:.4f}'.format(results[0]))\n", + "print('Accuracy: {:.2f}%'.format(results[1]*100))" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Evaluasi train data\n", + "79/79 [==============================] - 54s 679ms/step - loss: 0.0818 - accuracy: 0.9766\n", + "Loss: 0.0818\n", + "Accuracy: 97.66%\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CgGQ7GVD68VH", + "outputId": "094dc779-154d-41e7-acf9-94c19014688e" + }, + "source": [ + "# Evaluasi model pada validation data\n", + "print(\"Evaluasi validation data\")\n", + "results = model.evaluate(validation_generator, batch_size=32)\n", + "print('Loss: {:.4f}'.format(results[0]))\n", + "print('Accuracy: {:.2f}%'.format(results[1]*100))" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Evaluasi validation data\n", + "12/12 [==============================] - 6s 522ms/step - loss: 0.0468 - accuracy: 0.9785\n", + "Loss: 0.0468\n", + "Accuracy: 97.85%\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Hk98u9MfG_1h" + }, + "source": [ + "# Plotting Loss and Accuracy" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + }, + "id": "otJ9Cz4rly19", + "outputId": "be82770a-d500-498c-cef6-2bc785736737" + }, + "source": [ + "fig, ax = plt.subplots(1, 2)\n", + "fig.set_size_inches(12,4)\n", + "\n", + "# Define accuracy\n", + "acc = history.history['accuracy']\n", + "val_acc = history.history['val_accuracy']\n", + "\n", + "# Define loss\n", + "loss = history.history['loss']\n", + "val_loss = history.history['val_loss']\n", + "\n", + "epochs = range(len(acc))\n", + "\n", + "# Plotting accuracy\n", + "ax[0].plot(epochs, acc, 'r', label='Training Accuracy')\n", + "ax[0].plot(epochs, val_acc, 'b', label='Validation Accuracy')\n", + "ax[0].set_title('Training and Validation Accuracy')\n", + "ax[0].set_xlabel('Epoch')\n", + "ax[0].set_ylabel('Accuracy')\n", + "ax[0].legend(loc='upper left')\n", + "\n", + "# Plotting loss \n", + "ax[1].plot(epochs, loss, 'r', label='Training Loss')\n", + "ax[1].plot(epochs, val_loss, 'b', label='Validation Loss')\n", + "ax[1].set_title('Training and Validation Loss')\n", + "ax[1].set_xlabel('Epoch')\n", + "ax[1].set_ylabel('Loss')\n", + "ax[1].legend(loc='upper left')\n", + "\n", + "plt.show()" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RbdHBF4NHrpe" + }, + "source": [ + "# Model Prediction" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2rTZgG1ZT8Lb" + }, + "source": [ + "def predict_image(image_upload, model = model):\n", + " im = image_upload\n", + " im_array = np.asarray(im)\n", + " im_array = im_array*(1/225)\n", + " im_input = tf.reshape(im_array, shape = [1, 150, 150, 3])\n", + "\n", + " predict_array = model.predict(im_input)[0]\n", + "\n", + " import pandas as pd\n", + " df = pd.DataFrame(predict_array)\n", + " df = df.rename({0:'Probability'}, axis = 'columns')\n", + " prod = ['Paper', 'Rock', 'Scissors']\n", + " df['Product'] = prod\n", + " df = df[['Product', 'Probability']]\n", + "\n", + " predict_label = np.argmax(model.predict(im_input))\n", + "\n", + " if predict_label == 0:\n", + " predict_product = 'Paper'\n", + " elif predict_label == 1:\n", + " predict_product = 'Rock'\n", + " else:\n", + " predict_product = 'Scissor'\n", + "\n", + " return predict_product, df" + ], + "execution_count": 23, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "tGhi_CF6dvPL", + "colab": { + "resources": { + "http://localhost:8080/nbextensions/google.colab/files.js": { + "data": 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", + "ok": true, + "headers": [ + [ + "content-type", + "application/javascript" + ] + ], + "status": 200, + "status_text": "" + } + }, + "base_uri": "https://localhost:8080/", + "height": 528 + }, + "outputId": "6e71a859-70cd-44b8-8412-62daa20da2da" + }, + "source": [ + "uploaded = files.upload()\n", + "\n", + "for fn in uploaded.keys():\n", + " path = fn\n", + " img = image.load_img(path, target_size=(150,150))\n", + " imgplot = plt.imshow(img)\n", + " x = image.img_to_array(img)\n", + " x = np.expand_dims(x, axis=0)\n", + " img = np.vstack([x])\n", + "\n", + "label, df = predict_image(img)\n", + "\n", + "print('\\n')\n", + "plt.show()\n", + "print(\"\\nThe image is detected as \" + label)\n", + "print('\\n')\n", + "print(df)\n", + "print('\\n')" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving paper.jpg to paper (3).jpg\n", + "\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "The image is detected as Scissor\n", + "\n", + "\n", + " Product Probability\n", + "0 Paper 0.037916\n", + "1 Rock 0.001240\n", + "2 Scissors 0.960844\n", + "\n", + "\n" + ] + } + ] + } + ] +} \ No newline at end of file