├── .github └── ISSUE_TEMPLATE │ └── feature_request.md ├── 1 - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning ├── Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning Certificate.pdf ├── Week 1 │ ├── Course 1 Week 1.ipynb │ ├── Week 1 Quiz.pdf │ └── Week 1 Quiz.png ├── Week 2 │ ├── Course 1 Week 2.ipynb │ ├── Week 2 Quiz.pdf │ └── Week 2 Quiz.png ├── Week 3 │ ├── Course 1 Week 3.ipynb │ ├── Week 3 Quiz.pdf │ └── Week 3 Quiz.png └── Week 4 │ ├── Course 1 Week 4.ipynb │ ├── Week 4 Quiz.pdf │ └── Week 4 Quiz.png ├── 2 - Convolutional Neural Networks in TensorFlow ├── Convolutional Neural Networks in TensorFlow Certificate.pdf ├── Week 1 │ ├── Course 2 Week 1.ipynb │ ├── Week 1 Quiz.pdf │ └── Week 1 Quiz.png ├── Week 2 │ ├── Course 2 Week 2.ipynb │ ├── Week 2 Quiz.pdf │ └── Week 2 Quiz.png ├── Week 3 │ ├── Course 2 Week 3.ipynb │ ├── Week 3 Quiz.pdf │ └── Week 3 Quiz.png └── Week 4 │ ├── Course 2 Week 4.ipynb │ ├── Week 4 Quiz.pdf │ └── Week 4 Quiz.png ├── 3 - Natural Langu age Processing in TensorFlow ├── Natural Langu age Processing in TensorFlow.pdf ├── Week 1 │ ├── Course 3 Week 1.ipynb │ ├── Week 1 Quiz.pdf │ └── Week 1 Quiz.png ├── Week 2 │ ├── Course 3 Week 2.ipynb │ ├── Week 2 Quiz.pdf │ └── Week 2 Quiz.png ├── Week 3 │ ├── Course 3 Week 3.ipynb │ ├── Week 3 Quiz.pdf │ └── Week 3 Quiz.png └── Week 4 │ ├── Course 3 Week 4.ipynb │ ├── Week 4 Quiz.pdf │ └── Week 4 Quiz.png ├── 4 - Sequences, Time Series and Prediction ├── Sequ ences, Time Series and Prediction Certificate.pdf ├── Week 1 │ ├── Course 4 Week 1.ipynb │ ├── Week 1 Quiz.pdf │ └── Week 1 Quiz.png ├── Week 2 │ ├── Course 4 Week 2.ipynb │ ├── Week 2 Quiz.pdf │ └── Week 2 Quiz.png ├── Week 3 │ ├── Course 4 Week 3.ipynb │ ├── Week 3 Quiz.pdf │ └── Week 3 Quiz.png └── Week 4 │ ├── Course 4 Week 4.ipynb │ ├── Week 4 Quiz.pdf │ └── Week 4 Quiz.png ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── DeepLearning.AI TensorFlow Developer Professional Certificate.pdf ├── DeepLearning.AI TensorFlow Developer Course Certificate.png ├── LICENSE └── README.md /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest an idea for this project 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Is your feature request related to a problem? Please describe.** 11 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] 12 | 13 | **Describe the solution you'd like** 14 | A clear and concise description of what you want to happen. 15 | 16 | **Describe alternatives you've considered** 17 | A clear and concise description of any alternative solutions or features you've considered. 18 | 19 | **Additional context** 20 | Add any other context or screenshots about the feature request here. 21 | -------------------------------------------------------------------------------- /1 - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning/Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning Certificate.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/1 - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning/Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning Certificate.pdf -------------------------------------------------------------------------------- /1 - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning/Week 1/Week 1 Quiz.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/1 - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning/Week 1/Week 1 Quiz.pdf -------------------------------------------------------------------------------- /1 - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning/Week 1/Week 1 Quiz.png: -------------------------------------------------------------------------------- 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as tf\n", 25 | "\n", 26 | "class myCallback(tf.keras.callbacks.Callback):\n", 27 | " def on_epoch_end(self, epoch, logs={}):\n", 28 | " if(logs.get('acc')>0.99):\n", 29 | " print(\"\\nReached 99% accuracy so cancelling training!\")\n", 30 | " self.model.stop_training = True\n", 31 | "\n", 32 | "mnist = tf.keras.datasets.mnist\n", 33 | "\n", 34 | "(x_train, y_train),(x_test, y_test) = mnist.load_data()\n", 35 | "x_train, x_test = x_train / 255.0, x_test / 255.0\n", 36 | "\n", 37 | "callbacks = myCallback()\n", 38 | "\n", 39 | "model = tf.keras.models.Sequential([\n", 40 | " tf.keras.layers.Flatten(input_shape=(28, 28)),\n", 41 | " tf.keras.layers.Dense(512, activation=tf.nn.relu),\n", 42 | " tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n", 43 | "])\n", 44 | "model.compile(optimizer='adam',\n", 45 | " loss='sparse_categorical_crossentropy',\n", 46 | " metrics=['accuracy'])\n", 47 | "\n", 48 | "model.fit(x_train, y_train, epochs=10, callbacks=[callbacks])" 49 | ], 50 | "execution_count": 0, 51 | "outputs": [] 52 | } 53 | ] 54 | } -------------------------------------------------------------------------------- /1 - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning/Week 2/Week 2 Quiz.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/1 - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning/Week 2/Week 2 Quiz.pdf -------------------------------------------------------------------------------- /1 - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning/Week 2/Week 2 Quiz.png: -------------------------------------------------------------------------------- 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training!\")\n", 19 | " self.model.stop_training = True\n", 20 | "\n", 21 | "callbacks = myCallback()\n", 22 | "mnist = tf.keras.datasets.mnist\n", 23 | "(training_images, training_labels), (test_images, test_labels) = mnist.load_data()\n", 24 | "training_images=training_images.reshape(60000, 28, 28, 1)\n", 25 | "training_images=training_images / 255.0\n", 26 | "test_images = test_images.reshape(10000, 28, 28, 1)\n", 27 | "test_images=test_images/255.0\n", 28 | "model = tf.keras.models.Sequential([\n", 29 | " tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28, 28, 1)),\n", 30 | " tf.keras.layers.MaxPooling2D(2, 2),\n", 31 | " tf.keras.layers.Flatten(),\n", 32 | " tf.keras.layers.Dense(128, activation='relu'),\n", 33 | " tf.keras.layers.Dense(10, activation='softmax')\n", 34 | "])\n", 35 | "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", 36 | "model.fit(training_images, training_labels, epochs=10, callbacks=[callbacks])\n" 37 | ] 38 | } 39 | ], 40 | "metadata": { 41 | "colab": { 42 | "name": "Exercise 2 - Answer.ipynb", 43 | "provenance": [] 44 | }, 45 | "kernelspec": { 46 | "display_name": "Python 3", 47 | "language": "python", 48 | "name": "python3" 49 | }, 50 | "language_info": { 51 | "codemirror_mode": { 52 | "name": "ipython", 53 | "version": 3 54 | }, 55 | "file_extension": ".py", 56 | "mimetype": "text/x-python", 57 | "name": "python", 58 | "nbconvert_exporter": "python", 59 | "pygments_lexer": "ipython3", 60 | "version": "3.5.6" 61 | } 62 | }, 63 | "nbformat": 4, 64 | "nbformat_minor": 1 65 | } 66 | -------------------------------------------------------------------------------- /1 - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning/Week 3/Week 3 Quiz.pdf: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 0, 6 | "metadata": { 7 | "colab": {}, 8 | "colab_type": "code", 9 | "id": "3NFuMFYXtwsT" 10 | }, 11 | "outputs": [], 12 | "source": [ 13 | "import tensorflow as tf\n", 14 | "import os\n", 15 | "import zipfile\n", 16 | "\n", 17 | "DESIRED_ACCURACY = 0.999\n", 18 | "\n", 19 | "!wget --no-check-certificate \\\n", 20 | " \"https://storage.googleapis.com/laurencemoroney-blog.appspot.com/happy-or-sad.zip\" \\\n", 21 | " -O \"/tmp/happy-or-sad.zip\"\n", 22 | "\n", 23 | "zip_ref = zipfile.ZipFile(\"/tmp/happy-or-sad.zip\", 'r')\n", 24 | "zip_ref.extractall(\"/tmp/h-or-s\")\n", 25 | "zip_ref.close()\n", 26 | "\n", 27 | "class myCallback(tf.keras.callbacks.Callback):\n", 28 | " def on_epoch_end(self, epoch, logs={}):\n", 29 | " if(logs.get('acc')>DESIRED_ACCURACY):\n", 30 | " print(\"\\nReached 99.9% accuracy so cancelling training!\")\n", 31 | " self.model.stop_training = True\n", 32 | "\n", 33 | "callbacks = myCallback()\n" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 0, 39 | "metadata": { 40 | "colab": {}, 41 | "colab_type": "code", 42 | "id": "eUcNTpra1FK0" 43 | }, 44 | "outputs": [], 45 | "source": [ 46 | "model = tf.keras.models.Sequential([\n", 47 | " tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 3)),\n", 48 | " tf.keras.layers.MaxPooling2D(2, 2),\n", 49 | " tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n", 50 | " tf.keras.layers.MaxPooling2D(2,2),\n", 51 | " tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n", 52 | " tf.keras.layers.MaxPooling2D(2,2),\n", 53 | " tf.keras.layers.Flatten(),\n", 54 | " tf.keras.layers.Dense(512, activation='relu'),\n", 55 | " tf.keras.layers.Dense(1, activation='sigmoid')\n", 56 | "])\n", 57 | "\n", 58 | "from tensorflow.keras.optimizers import RMSprop\n", 59 | "\n", 60 | "model.compile(loss='binary_crossentropy',\n", 61 | " optimizer=RMSprop(lr=0.001),\n", 62 | " metrics=['acc'])" 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 0, 68 | "metadata": { 69 | "colab": {}, 70 | "colab_type": "code", 71 | "id": "sSaPPUe_z_OU" 72 | }, 73 | "outputs": [], 74 | "source": [ 75 | "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", 76 | "\n", 77 | "train_datagen = ImageDataGenerator(rescale=1/255)\n", 78 | "\n", 79 | "train_generator = train_datagen.flow_from_directory(\n", 80 | " \"/tmp/h-or-s\", \n", 81 | " target_size=(150, 150), \n", 82 | " batch_size=10,\n", 83 | " class_mode='binary')\n", 84 | "\n", 85 | "# Expected output: 'Found 80 images belonging to 2 classes'" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": 0, 91 | "metadata": { 92 | "colab": {}, 93 | "colab_type": "code", 94 | "id": "0imravDn0Ajz" 95 | }, 96 | "outputs": [], 97 | "source": [ 98 | "history = model.fit_generator(\n", 99 | " train_generator,\n", 100 | " steps_per_epoch=2, \n", 101 | " epochs=15,\n", 102 | " verbose=1,\n", 103 | " callbacks=[callbacks])" 104 | ] 105 | } 106 | ], 107 | "metadata": { 108 | "accelerator": "GPU", 109 | "colab": { 110 | "name": "Exercise4-Answer.ipynb", 111 | "provenance": [] 112 | }, 113 | "kernelspec": { 114 | "display_name": "Python 3", 115 | "language": "python", 116 | "name": "python3" 117 | }, 118 | "language_info": { 119 | "codemirror_mode": { 120 | "name": "ipython", 121 | "version": 3 122 | }, 123 | "file_extension": ".py", 124 | "mimetype": "text/x-python", 125 | "name": "python", 126 | "nbconvert_exporter": "python", 127 | "pygments_lexer": "ipython3", 128 | "version": "3.5.6" 129 | } 130 | }, 131 | "nbformat": 4, 132 | "nbformat_minor": 1 133 | } 134 | -------------------------------------------------------------------------------- /1 - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning/Week 4/Week 4 Quiz.pdf: 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TensorFlow/Convolutional Neural Networks in TensorFlow Certificate.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/2 - Convolutional Neural Networks in TensorFlow/Convolutional Neural Networks in TensorFlow Certificate.pdf -------------------------------------------------------------------------------- /2 - Convolutional Neural Networks in TensorFlow/Week 1/Course 2 Week 1.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 0, 6 | "metadata": { 7 | "colab": {}, 8 | "colab_type": "code", 9 | "id": "dn-6c02VmqiN" 10 | }, 11 | "outputs": [], 12 | "source": [ 13 | "import os\n", 14 | "import zipfile\n", 15 | "import random\n", 16 | "import tensorflow as tf\n", 17 | "from tensorflow.keras.optimizers import RMSprop\n", 18 | "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", 19 | "from shutil import copyfile" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 0, 25 | "metadata": { 26 | "colab": {}, 27 | "colab_type": "code", 28 | "id": "3sd9dQWa23aj" 29 | }, 30 | "outputs": [], 31 | "source": [ 32 | "# If the URL doesn't work, visit https://www.microsoft.com/en-us/download/confirmation.aspx?id=54765\n", 33 | "# And right click on the 'Download Manually' link to get a new URL to the dataset\n", 34 | "\n", 35 | "# Note: This is a very large dataset and will take time to download\n", 36 | "\n", 37 | "!wget --no-check-certificate \\\n", 38 | " \"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\" \\\n", 39 | " -O \"/tmp/cats-and-dogs.zip\"\n", 40 | "\n", 41 | "local_zip = '/tmp/cats-and-dogs.zip'\n", 42 | "zip_ref = zipfile.ZipFile(local_zip, 'r')\n", 43 | "zip_ref.extractall('/tmp')\n", 44 | "zip_ref.close()\n" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 0, 50 | "metadata": { 51 | "colab": {}, 52 | "colab_type": "code", 53 | "id": "DM851ZmN28J3" 54 | }, 55 | "outputs": [], 56 | "source": [ 57 | "print(len(os.listdir('/tmp/PetImages/Cat/')))\n", 58 | "print(len(os.listdir('/tmp/PetImages/Dog/')))\n", 59 | "\n", 60 | "# Expected Output:\n", 61 | "# 12501\n", 62 | "# 12501" 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 0, 68 | "metadata": { 69 | "colab": {}, 70 | "colab_type": "code", 71 | "id": "F-QkLjxpmyK2" 72 | }, 73 | "outputs": [], 74 | "source": [ 75 | "try:\n", 76 | " os.mkdir('/tmp/cats-v-dogs')\n", 77 | " os.mkdir('/tmp/cats-v-dogs/training')\n", 78 | " os.mkdir('/tmp/cats-v-dogs/testing')\n", 79 | " os.mkdir('/tmp/cats-v-dogs/training/cats')\n", 80 | " os.mkdir('/tmp/cats-v-dogs/training/dogs')\n", 81 | " os.mkdir('/tmp/cats-v-dogs/testing/cats')\n", 82 | " os.mkdir('/tmp/cats-v-dogs/testing/dogs')\n", 83 | "except OSError:\n", 84 | " pass" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 0, 90 | "metadata": { 91 | "colab": {}, 92 | "colab_type": "code", 93 | "id": "zvSODo0f9LaU" 94 | }, 95 | "outputs": [], 96 | "source": [ 97 | "def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):\n", 98 | " files = []\n", 99 | " for filename in os.listdir(SOURCE):\n", 100 | " file = SOURCE + filename\n", 101 | " if os.path.getsize(file) > 0:\n", 102 | " files.append(filename)\n", 103 | " else:\n", 104 | " print(filename + \" is zero length, so ignoring.\")\n", 105 | "\n", 106 | " training_length = int(len(files) * SPLIT_SIZE)\n", 107 | " testing_length = int(len(files) - training_length)\n", 108 | " shuffled_set = random.sample(files, len(files))\n", 109 | " training_set = shuffled_set[0:training_length]\n", 110 | " testing_set = shuffled_set[-testing_length:]\n", 111 | "\n", 112 | " for filename in training_set:\n", 113 | " this_file = SOURCE + filename\n", 114 | " destination = TRAINING + filename\n", 115 | " copyfile(this_file, destination)\n", 116 | "\n", 117 | " for filename in testing_set:\n", 118 | " this_file = SOURCE + filename\n", 119 | " destination = TESTING + filename\n", 120 | " copyfile(this_file, destination)\n", 121 | "\n", 122 | "\n", 123 | "CAT_SOURCE_DIR = \"/tmp/PetImages/Cat/\"\n", 124 | "TRAINING_CATS_DIR = \"/tmp/cats-v-dogs/training/cats/\"\n", 125 | "TESTING_CATS_DIR = \"/tmp/cats-v-dogs/testing/cats/\"\n", 126 | "DOG_SOURCE_DIR = \"/tmp/PetImages/Dog/\"\n", 127 | "TRAINING_DOGS_DIR = \"/tmp/cats-v-dogs/training/dogs/\"\n", 128 | "TESTING_DOGS_DIR = \"/tmp/cats-v-dogs/testing/dogs/\"\n", 129 | "\n", 130 | "split_size = .9\n", 131 | "split_data(CAT_SOURCE_DIR, TRAINING_CATS_DIR, TESTING_CATS_DIR, split_size)\n", 132 | "split_data(DOG_SOURCE_DIR, TRAINING_DOGS_DIR, TESTING_DOGS_DIR, split_size)\n", 133 | "\n", 134 | "# Expected output\n", 135 | "# 666.jpg is zero length, so ignoring\n", 136 | "# 11702.jpg is zero length, so ignoring" 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": 0, 142 | "metadata": { 143 | "colab": {}, 144 | "colab_type": "code", 145 | "id": "hwHXFhVG3786" 146 | }, 147 | "outputs": [], 148 | "source": [ 149 | "print(len(os.listdir('/tmp/cats-v-dogs/training/cats/')))\n", 150 | "print(len(os.listdir('/tmp/cats-v-dogs/training/dogs/')))\n", 151 | "print(len(os.listdir('/tmp/cats-v-dogs/testing/cats/')))\n", 152 | "print(len(os.listdir('/tmp/cats-v-dogs/testing/dogs/')))\n", 153 | "\n", 154 | "# Expected output:\n", 155 | "# 11250\n", 156 | "# 11250\n", 157 | "# 1250\n", 158 | "# 1250" 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": 0, 164 | "metadata": { 165 | "colab": {}, 166 | "colab_type": "code", 167 | "id": "-BQrav4anTmj" 168 | }, 169 | "outputs": [], 170 | "source": [ 171 | "model = tf.keras.models.Sequential([\n", 172 | " tf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n", 173 | " tf.keras.layers.MaxPooling2D(2, 2),\n", 174 | " tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n", 175 | " tf.keras.layers.MaxPooling2D(2, 2),\n", 176 | " tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n", 177 | " tf.keras.layers.MaxPooling2D(2, 2),\n", 178 | " tf.keras.layers.Flatten(),\n", 179 | " tf.keras.layers.Dense(512, activation='relu'),\n", 180 | " tf.keras.layers.Dense(1, activation='sigmoid')\n", 181 | "])\n", 182 | "\n", 183 | "model.compile(optimizer=RMSprop(lr=0.001), loss='binary_crossentropy', metrics=['acc'])\n" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": 0, 189 | "metadata": { 190 | "colab": {}, 191 | "colab_type": "code", 192 | "id": "fQrZfVgz4j2g" 193 | }, 194 | "outputs": [], 195 | "source": [ 196 | "\n", 197 | "TRAINING_DIR = \"/tmp/cats-v-dogs/training/\"\n", 198 | "train_datagen = ImageDataGenerator(rescale=1.0/255.)\n", 199 | "train_generator = train_datagen.flow_from_directory(TRAINING_DIR,\n", 200 | " batch_size=100,\n", 201 | " class_mode='binary',\n", 202 | " target_size=(150, 150))\n", 203 | "\n", 204 | "VALIDATION_DIR = \"/tmp/cats-v-dogs/testing/\"\n", 205 | "validation_datagen = ImageDataGenerator(rescale=1.0/255.)\n", 206 | "validation_generator = validation_datagen.flow_from_directory(VALIDATION_DIR,\n", 207 | " batch_size=100,\n", 208 | " class_mode='binary',\n", 209 | " target_size=(150, 150))\n", 210 | "\n", 211 | "# Expected Output:\n", 212 | "# Found 22498 images belonging to 2 classes.\n", 213 | "# Found 2500 images belonging to 2 classes." 214 | ] 215 | }, 216 | { 217 | "cell_type": "code", 218 | "execution_count": 0, 219 | "metadata": { 220 | "colab": {}, 221 | "colab_type": "code", 222 | "id": "5qE1G6JB4fMn" 223 | }, 224 | "outputs": [], 225 | "source": [ 226 | "# Note that this may take some time.\n", 227 | "history = model.fit_generator(train_generator,\n", 228 | " epochs=50,\n", 229 | " verbose=1,\n", 230 | " validation_data=validation_generator)" 231 | ] 232 | }, 233 | { 234 | "cell_type": "code", 235 | "execution_count": 0, 236 | "metadata": { 237 | "colab": {}, 238 | "colab_type": "code", 239 | "id": "MWZrJN4-65RC" 240 | }, 241 | "outputs": [], 242 | "source": [ 243 | "%matplotlib inline\n", 244 | "\n", 245 | "import matplotlib.image as mpimg\n", 246 | "import matplotlib.pyplot as plt\n", 247 | "\n", 248 | "#-----------------------------------------------------------\n", 249 | "# Retrieve a list of list results on training and test data\n", 250 | "# sets for each training epoch\n", 251 | "#-----------------------------------------------------------\n", 252 | "acc=history.history['acc']\n", 253 | "val_acc=history.history['val_acc']\n", 254 | "loss=history.history['loss']\n", 255 | "val_loss=history.history['val_loss']\n", 256 | "\n", 257 | "epochs=range(len(acc)) # Get number of epochs\n", 258 | "\n", 259 | "#------------------------------------------------\n", 260 | "# Plot training and validation accuracy per epoch\n", 261 | "#------------------------------------------------\n", 262 | "plt.plot(epochs, acc, 'r', \"Training Accuracy\")\n", 263 | "plt.plot(epochs, val_acc, 'b', \"Validation Accuracy\")\n", 264 | "plt.title('Training and validation accuracy')\n", 265 | "plt.figure()\n", 266 | "\n", 267 | "#------------------------------------------------\n", 268 | "# Plot training and validation loss per epoch\n", 269 | "#------------------------------------------------\n", 270 | "plt.plot(epochs, loss, 'r', \"Training Loss\")\n", 271 | "plt.plot(epochs, val_loss, 'b', \"Validation Loss\")\n", 272 | "plt.figure()\n", 273 | "\n", 274 | "\n", 275 | "# Desired output. Charts with training and validation metrics. No crash :)" 276 | ] 277 | }, 278 | { 279 | "cell_type": "code", 280 | "execution_count": 0, 281 | "metadata": { 282 | "colab": {}, 283 | "colab_type": "code", 284 | "id": "LqL6FYUrtXpf" 285 | }, 286 | "outputs": [], 287 | "source": [ 288 | "# Here's a codeblock just for fun. You should be able to upload an image here \n", 289 | "# and have it classified without crashing\n", 290 | "import numpy as np\n", 291 | "from google.colab import files\n", 292 | "from keras.preprocessing import image\n", 293 | "\n", 294 | "uploaded = files.upload()\n", 295 | "\n", 296 | "for fn in uploaded.keys():\n", 297 | " \n", 298 | " # predicting images\n", 299 | " path = '/content/' + fn\n", 300 | " img = image.load_img(path, target_size=(150, 150))\n", 301 | " x = image.img_to_array(img)\n", 302 | " x = np.expand_dims(x, axis=0)\n", 303 | "\n", 304 | " images = np.vstack([x])\n", 305 | " classes = model.predict(images, batch_size=10)\n", 306 | " print(classes[0])\n", 307 | " if classes[0]>0.5:\n", 308 | " print(fn + \" is a dog\")\n", 309 | " else:\n", 310 | " print(fn + \" is a cat\")" 311 | ] 312 | } 313 | ], 314 | "metadata": { 315 | "accelerator": "GPU", 316 | "colab": { 317 | "name": "Exercise 5 - Answer.ipynb", 318 | "provenance": [] 319 | }, 320 | "kernelspec": { 321 | "display_name": "Python 3", 322 | "language": "python", 323 | "name": "python3" 324 | }, 325 | "language_info": { 326 | "codemirror_mode": { 327 | "name": "ipython", 328 | "version": 3 329 | }, 330 | "file_extension": ".py", 331 | "mimetype": "text/x-python", 332 | "name": "python", 333 | "nbconvert_exporter": "python", 334 | "pygments_lexer": "ipython3", 335 | "version": "3.5.6" 336 | } 337 | }, 338 | "nbformat": 4, 339 | "nbformat_minor": 1 340 | } 341 | -------------------------------------------------------------------------------- /2 - Convolutional Neural Networks in TensorFlow/Week 1/Week 1 Quiz.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/2 - Convolutional Neural Networks in TensorFlow/Week 1/Week 1 Quiz.pdf -------------------------------------------------------------------------------- /2 - Convolutional Neural Networks in TensorFlow/Week 1/Week 1 Quiz.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/2 - Convolutional Neural Networks in TensorFlow/Week 1/Week 1 Quiz.png -------------------------------------------------------------------------------- /2 - Convolutional Neural Networks in TensorFlow/Week 2/Course 2 Week 2.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 0, 6 | "metadata": { 7 | "colab": {}, 8 | "colab_type": "code", 9 | "id": "dn-6c02VmqiN" 10 | }, 11 | "outputs": [], 12 | "source": [ 13 | "import os\n", 14 | "import zipfile\n", 15 | "import random\n", 16 | "import tensorflow as tf\n", 17 | "from tensorflow.keras.optimizers import RMSprop\n", 18 | "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", 19 | "from shutil import copyfile" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 0, 25 | "metadata": { 26 | "colab": {}, 27 | "colab_type": "code", 28 | "id": "3sd9dQWa23aj" 29 | }, 30 | "outputs": [], 31 | "source": [ 32 | "# If the URL doesn't work, visit https://www.microsoft.com/en-us/download/confirmation.aspx?id=54765\n", 33 | "# And right click on the 'Download Manually' link to get a new URL to the dataset\n", 34 | "\n", 35 | "# Note: This is a very large dataset and will take time to download\n", 36 | "\n", 37 | "!wget --no-check-certificate \\\n", 38 | " \"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\" \\\n", 39 | " -O \"/tmp/cats-and-dogs.zip\"\n", 40 | "\n", 41 | "local_zip = '/tmp/cats-and-dogs.zip'\n", 42 | "zip_ref = zipfile.ZipFile(local_zip, 'r')\n", 43 | "zip_ref.extractall('/tmp')\n", 44 | "zip_ref.close()\n" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 0, 50 | "metadata": { 51 | "colab": {}, 52 | "colab_type": "code", 53 | "id": "DM851ZmN28J3" 54 | }, 55 | "outputs": [], 56 | "source": [ 57 | "print(len(os.listdir('/tmp/PetImages/Cat/')))\n", 58 | "print(len(os.listdir('/tmp/PetImages/Dog/')))\n", 59 | "\n", 60 | "# Expected Output:\n", 61 | "# 12501\n", 62 | "# 12501" 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 0, 68 | "metadata": { 69 | "colab": {}, 70 | "colab_type": "code", 71 | "id": "F-QkLjxpmyK2" 72 | }, 73 | "outputs": [], 74 | "source": [ 75 | "try:\n", 76 | " os.mkdir('/tmp/cats-v-dogs')\n", 77 | " os.mkdir('/tmp/cats-v-dogs/training')\n", 78 | " os.mkdir('/tmp/cats-v-dogs/testing')\n", 79 | " os.mkdir('/tmp/cats-v-dogs/training/cats')\n", 80 | " os.mkdir('/tmp/cats-v-dogs/training/dogs')\n", 81 | " os.mkdir('/tmp/cats-v-dogs/testing/cats')\n", 82 | " os.mkdir('/tmp/cats-v-dogs/testing/dogs')\n", 83 | "except OSError:\n", 84 | " pass" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 0, 90 | "metadata": { 91 | "colab": {}, 92 | "colab_type": "code", 93 | "id": "zvSODo0f9LaU" 94 | }, 95 | "outputs": [], 96 | "source": [ 97 | "def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):\n", 98 | " files = []\n", 99 | " for filename in os.listdir(SOURCE):\n", 100 | " file = SOURCE + filename\n", 101 | " if os.path.getsize(file) > 0:\n", 102 | " files.append(filename)\n", 103 | " else:\n", 104 | " print(filename + \" is zero length, so ignoring.\")\n", 105 | "\n", 106 | " training_length = int(len(files) * SPLIT_SIZE)\n", 107 | " testing_length = int(len(files) - training_length)\n", 108 | " shuffled_set = random.sample(files, len(files))\n", 109 | " training_set = shuffled_set[0:training_length]\n", 110 | " testing_set = shuffled_set[:testing_length]\n", 111 | "\n", 112 | " for filename in training_set:\n", 113 | " this_file = SOURCE + filename\n", 114 | " destination = TRAINING + filename\n", 115 | " copyfile(this_file, destination)\n", 116 | "\n", 117 | " for filename in testing_set:\n", 118 | " this_file = SOURCE + filename\n", 119 | " destination = TESTING + filename\n", 120 | " copyfile(this_file, destination)\n", 121 | "\n", 122 | "\n", 123 | "CAT_SOURCE_DIR = \"/tmp/PetImages/Cat/\"\n", 124 | "TRAINING_CATS_DIR = \"/tmp/cats-v-dogs/training/cats/\"\n", 125 | "TESTING_CATS_DIR = \"/tmp/cats-v-dogs/testing/cats/\"\n", 126 | "DOG_SOURCE_DIR = \"/tmp/PetImages/Dog/\"\n", 127 | "TRAINING_DOGS_DIR = \"/tmp/cats-v-dogs/training/dogs/\"\n", 128 | "TESTING_DOGS_DIR = \"/tmp/cats-v-dogs/testing/dogs/\"\n", 129 | "\n", 130 | "split_size = .9\n", 131 | "split_data(CAT_SOURCE_DIR, TRAINING_CATS_DIR, TESTING_CATS_DIR, split_size)\n", 132 | "split_data(DOG_SOURCE_DIR, TRAINING_DOGS_DIR, TESTING_DOGS_DIR, split_size)\n", 133 | "\n", 134 | "# Expected output\n", 135 | "# 666.jpg is zero length, so ignoring\n", 136 | "# 11702.jpg is zero length, so ignoring" 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": 0, 142 | "metadata": { 143 | "colab": {}, 144 | "colab_type": "code", 145 | "id": "hwHXFhVG3786" 146 | }, 147 | "outputs": [], 148 | "source": [ 149 | "print(len(os.listdir('/tmp/cats-v-dogs/training/cats/')))\n", 150 | "print(len(os.listdir('/tmp/cats-v-dogs/training/dogs/')))\n", 151 | "print(len(os.listdir('/tmp/cats-v-dogs/testing/cats/')))\n", 152 | "print(len(os.listdir('/tmp/cats-v-dogs/testing/dogs/')))\n", 153 | "\n", 154 | "# Expected output:\n", 155 | "# 11250\n", 156 | "# 11250\n", 157 | "# 1250\n", 158 | "# 1250" 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": 0, 164 | "metadata": { 165 | "colab": {}, 166 | "colab_type": "code", 167 | "id": "-BQrav4anTmj" 168 | }, 169 | "outputs": [], 170 | "source": [ 171 | "model = tf.keras.models.Sequential([\n", 172 | " tf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n", 173 | " tf.keras.layers.MaxPooling2D(2, 2),\n", 174 | " tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n", 175 | " tf.keras.layers.MaxPooling2D(2, 2),\n", 176 | " tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n", 177 | " tf.keras.layers.MaxPooling2D(2, 2),\n", 178 | " tf.keras.layers.Flatten(),\n", 179 | " tf.keras.layers.Dense(512, activation='relu'),\n", 180 | " tf.keras.layers.Dense(1, activation='sigmoid')\n", 181 | "])\n", 182 | "\n", 183 | "model.compile(optimizer=RMSprop(lr=0.001), loss='binary_crossentropy', metrics=['acc'])\n" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": 0, 189 | "metadata": { 190 | "colab": {}, 191 | "colab_type": "code", 192 | "id": "fQrZfVgz4j2g" 193 | }, 194 | "outputs": [], 195 | "source": [ 196 | "\n", 197 | "TRAINING_DIR = \"/tmp/cats-v-dogs/training/\"\n", 198 | "# Experiment with your own parameters here to really try to drive it to 99.9% accuracy or better\n", 199 | "train_datagen = ImageDataGenerator(rescale=1./255,\n", 200 | " rotation_range=40,\n", 201 | " width_shift_range=0.2,\n", 202 | " height_shift_range=0.2,\n", 203 | " shear_range=0.2,\n", 204 | " zoom_range=0.2,\n", 205 | " horizontal_flip=True,\n", 206 | " fill_mode='nearest')\n", 207 | "train_generator = train_datagen.flow_from_directory(TRAINING_DIR,\n", 208 | " batch_size=100,\n", 209 | " class_mode='binary',\n", 210 | " target_size=(150, 150))\n", 211 | "\n", 212 | "VALIDATION_DIR = \"/tmp/cats-v-dogs/testing/\"\n", 213 | "# Experiment with your own parameters here to really try to drive it to 99.9% accuracy or better\n", 214 | "validation_datagen = ImageDataGenerator(rescale=1./255,\n", 215 | " rotation_range=40,\n", 216 | " width_shift_range=0.2,\n", 217 | " height_shift_range=0.2,\n", 218 | " shear_range=0.2,\n", 219 | " zoom_range=0.2,\n", 220 | " horizontal_flip=True,\n", 221 | " fill_mode='nearest')\n", 222 | "validation_generator = validation_datagen.flow_from_directory(VALIDATION_DIR,\n", 223 | " batch_size=100,\n", 224 | " class_mode='binary',\n", 225 | " target_size=(150, 150))\n", 226 | "\n", 227 | "# Expected Output:\n", 228 | "# Found 22498 images belonging to 2 classes.\n", 229 | "# Found 2500 images belonging to 2 classes." 230 | ] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "execution_count": 0, 235 | "metadata": { 236 | "colab": {}, 237 | "colab_type": "code", 238 | "id": "5qE1G6JB4fMn" 239 | }, 240 | "outputs": [], 241 | "source": [ 242 | "# Note that this may take some time.\n", 243 | "history = model.fit_generator(train_generator,\n", 244 | " epochs=15,\n", 245 | " verbose=1,\n", 246 | " validation_data=validation_generator)" 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "execution_count": 0, 252 | "metadata": { 253 | "colab": {}, 254 | "colab_type": "code", 255 | "id": "MWZrJN4-65RC" 256 | }, 257 | "outputs": [], 258 | "source": [ 259 | "%matplotlib inline\n", 260 | "\n", 261 | "import matplotlib.image as mpimg\n", 262 | "import matplotlib.pyplot as plt\n", 263 | "\n", 264 | "#-----------------------------------------------------------\n", 265 | "# Retrieve a list of list results on training and test data\n", 266 | "# sets for each training epoch\n", 267 | "#-----------------------------------------------------------\n", 268 | "acc=history.history['acc']\n", 269 | "val_acc=history.history['val_acc']\n", 270 | "loss=history.history['loss']\n", 271 | "val_loss=history.history['val_loss']\n", 272 | "\n", 273 | "epochs=range(len(acc)) # Get number of epochs\n", 274 | "\n", 275 | "#------------------------------------------------\n", 276 | "# Plot training and validation accuracy per epoch\n", 277 | "#------------------------------------------------\n", 278 | "plt.plot(epochs, acc, 'r', \"Training Accuracy\")\n", 279 | "plt.plot(epochs, val_acc, 'b', \"Validation Accuracy\")\n", 280 | "plt.title('Training and validation accuracy')\n", 281 | "plt.figure()\n", 282 | "\n", 283 | "#------------------------------------------------\n", 284 | "# Plot training and validation loss per epoch\n", 285 | "#------------------------------------------------\n", 286 | "plt.plot(epochs, loss, 'r', \"Training Loss\")\n", 287 | "plt.plot(epochs, val_loss, 'b', \"Validation Loss\")\n", 288 | "plt.figure()\n", 289 | "\n", 290 | "\n", 291 | "# Desired output. Charts with training and validation metrics. No crash :)" 292 | ] 293 | }, 294 | { 295 | "cell_type": "code", 296 | "execution_count": 0, 297 | "metadata": { 298 | "colab": {}, 299 | "colab_type": "code", 300 | "id": "LqL6FYUrtXpf" 301 | }, 302 | "outputs": [], 303 | "source": [ 304 | "# Here's a codeblock just for fun. You should be able to upload an image here \n", 305 | "# and have it classified without crashing\n", 306 | "import numpy as np\n", 307 | "from google.colab import files\n", 308 | "from keras.preprocessing import image\n", 309 | "\n", 310 | "uploaded = files.upload()\n", 311 | "\n", 312 | "for fn in uploaded.keys():\n", 313 | " \n", 314 | " # predicting images\n", 315 | " path = '/content/' + fn\n", 316 | " img = image.load_img(path, target_size=(150, 150))\n", 317 | " x = image.img_to_array(img)\n", 318 | " x = np.expand_dims(x, axis=0)\n", 319 | "\n", 320 | " images = np.vstack([x])\n", 321 | " classes = model.predict(images, batch_size=10)\n", 322 | " print(classes[0])\n", 323 | " if classes[0]>0.5:\n", 324 | " print(fn + \" is a dog\")\n", 325 | " else:\n", 326 | " print(fn + \" is a cat\")" 327 | ] 328 | } 329 | ], 330 | "metadata": { 331 | "accelerator": "GPU", 332 | "colab": { 333 | "name": "Exercise 6 - Answer.ipynb", 334 | "provenance": [] 335 | }, 336 | "kernelspec": { 337 | "display_name": "Python 3", 338 | "language": "python", 339 | "name": "python3" 340 | }, 341 | "language_info": { 342 | "codemirror_mode": { 343 | "name": "ipython", 344 | "version": 3 345 | }, 346 | "file_extension": ".py", 347 | "mimetype": "text/x-python", 348 | "name": "python", 349 | "nbconvert_exporter": "python", 350 | "pygments_lexer": "ipython3", 351 | "version": "3.5.6" 352 | } 353 | }, 354 | "nbformat": 4, 355 | "nbformat_minor": 1 356 | } 357 | -------------------------------------------------------------------------------- /2 - Convolutional Neural Networks in TensorFlow/Week 2/Week 2 Quiz.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/2 - Convolutional Neural Networks in TensorFlow/Week 2/Week 2 Quiz.pdf -------------------------------------------------------------------------------- /2 - Convolutional Neural Networks in TensorFlow/Week 2/Week 2 Quiz.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/2 - Convolutional Neural Networks in TensorFlow/Week 2/Week 2 Quiz.png -------------------------------------------------------------------------------- /2 - Convolutional Neural Networks in TensorFlow/Week 3/Week 3 Quiz.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/2 - Convolutional Neural Networks in TensorFlow/Week 3/Week 3 Quiz.pdf -------------------------------------------------------------------------------- /2 - Convolutional Neural Networks in TensorFlow/Week 3/Week 3 Quiz.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/2 - Convolutional Neural Networks in TensorFlow/Week 3/Week 3 Quiz.png -------------------------------------------------------------------------------- /2 - Convolutional Neural Networks in TensorFlow/Week 4/Course 2 Week 4.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 0, 6 | "metadata": { 7 | "colab": {}, 8 | "colab_type": "code", 9 | "id": "wYtuKeK0dImp" 10 | }, 11 | "outputs": [], 12 | "source": [ 13 | "import csv\n", 14 | "import numpy as np\n", 15 | "import tensorflow as tf\n", 16 | "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", 17 | "from google.colab import files" 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": { 23 | "colab_type": "text", 24 | "id": "EmMyh9_mkDHF" 25 | }, 26 | "source": [ 27 | "The data for this exercise is available at: https://www.kaggle.com/datamunge/sign-language-mnist/home\n", 28 | "\n", 29 | "Sign up and download to find 2 CSV files: sign_mnist_test.csv and sign_mnist_train.csv -- You will upload both of them using this button before you can continue.\n" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 0, 35 | "metadata": { 36 | "colab": { 37 | "height": 106 38 | }, 39 | "colab_type": "code", 40 | "id": "IcLOZlnnc_N7", 41 | "outputId": "f902689f-06ab-422b-a15d-ceee438a19cb" 42 | }, 43 | "outputs": [ 44 | { 45 | "data": { 46 | "text/html": [ 47 | "\n", 48 | " \n", 49 | " \n", 50 | " Upload widget is only available when the cell has been executed in the\n", 51 | " current browser session. Please rerun this cell to enable.\n", 52 | " \n", 53 | " " 54 | ], 55 | "text/plain": [ 56 | "" 57 | ] 58 | }, 59 | "metadata": { 60 | "tags": [] 61 | }, 62 | "output_type": "display_data" 63 | }, 64 | { 65 | "name": "stdout", 66 | "output_type": "stream", 67 | "text": [ 68 | "Saving sign_mnist_test.csv to sign_mnist_test.csv\n", 69 | "Saving sign_mnist_train.csv to sign_mnist_train.csv\n" 70 | ] 71 | } 72 | ], 73 | "source": [ 74 | "uploaded=files.upload()" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": 0, 80 | "metadata": { 81 | "colab": { 82 | "height": 86 83 | }, 84 | "colab_type": "code", 85 | "id": "4kxw-_rmcnVu", 86 | "outputId": "3d62714a-acc3-4aef-98ec-10fee8e7ab50" 87 | }, 88 | "outputs": [ 89 | { 90 | "name": "stdout", 91 | "output_type": "stream", 92 | "text": [ 93 | "(27455, 28, 28)\n", 94 | "(27455,)\n", 95 | "(7172, 28, 28)\n", 96 | "(7172,)\n" 97 | ] 98 | } 99 | ], 100 | "source": [ 101 | "def get_data(filename):\n", 102 | " with open(filename) as training_file:\n", 103 | " csv_reader = csv.reader(training_file, delimiter=',')\n", 104 | " first_line = True\n", 105 | " temp_images = []\n", 106 | " temp_labels = []\n", 107 | " for row in csv_reader:\n", 108 | " if first_line:\n", 109 | " # print(\"Ignoring first line\")\n", 110 | " first_line = False\n", 111 | " else:\n", 112 | " temp_labels.append(row[0])\n", 113 | " image_data = row[1:785]\n", 114 | " image_data_as_array = np.array_split(image_data, 28)\n", 115 | " temp_images.append(image_data_as_array)\n", 116 | " images = np.array(temp_images).astype('float')\n", 117 | " labels = np.array(temp_labels).astype('float')\n", 118 | " return images, labels\n", 119 | "\n", 120 | "\n", 121 | "training_images, training_labels = get_data('sign_mnist_train.csv')\n", 122 | "testing_images, testing_labels = get_data('sign_mnist_test.csv')\n", 123 | "\n", 124 | "print(training_images.shape)\n", 125 | "print(training_labels.shape)\n", 126 | "print(testing_images.shape)\n", 127 | "print(testing_labels.shape)\n" 128 | ] 129 | }, 130 | { 131 | "cell_type": "code", 132 | "execution_count": 0, 133 | "metadata": { 134 | "colab": { 135 | "height": 52 136 | }, 137 | "colab_type": "code", 138 | "id": "awoqRpyZdQkD", 139 | "outputId": "a223a6f6-29f2-4f5f-8ccf-80f36d3368d1" 140 | }, 141 | "outputs": [ 142 | { 143 | "name": "stdout", 144 | "output_type": "stream", 145 | "text": [ 146 | "(27455, 28, 28, 1)\n", 147 | "(7172, 28, 28, 1)\n" 148 | ] 149 | } 150 | ], 151 | "source": [ 152 | "training_images = np.expand_dims(training_images, axis=3)\n", 153 | "testing_images = np.expand_dims(testing_images, axis=3)\n", 154 | "\n", 155 | "train_datagen = ImageDataGenerator(\n", 156 | " rescale=1. / 255,\n", 157 | " rotation_range=40,\n", 158 | " width_shift_range=0.2,\n", 159 | " height_shift_range=0.2,\n", 160 | " shear_range=0.2,\n", 161 | " zoom_range=0.2,\n", 162 | " horizontal_flip=True,\n", 163 | " fill_mode='nearest')\n", 164 | "\n", 165 | "validation_datagen = ImageDataGenerator(\n", 166 | " rescale=1. / 255)\n", 167 | "\n", 168 | "print(training_images.shape)\n", 169 | "print(testing_images.shape)" 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": 0, 175 | "metadata": { 176 | "colab": { 177 | "height": 570 178 | }, 179 | "colab_type": "code", 180 | "id": "Rmb7S32cgRqS", 181 | "outputId": "d1cbfc47-3a0a-4605-f1be-fa968032283d" 182 | }, 183 | "outputs": [ 184 | { 185 | "name": "stdout", 186 | "output_type": "stream", 187 | "text": [ 188 | "Epoch 1/15\n", 189 | "857/857 [==============================] - 21s 24ms/step - loss: 2.8540 - acc: 0.1562 - val_loss: 1.9644 - val_acc: 0.3100\n", 190 | "Epoch 2/15\n", 191 | "857/857 [==============================] - 46s 54ms/step - loss: 2.1131 - acc: 0.2188 - val_loss: 1.5558 - val_acc: 0.4855\n", 192 | "Epoch 3/15\n", 193 | "857/857 [==============================] - 40s 47ms/step - loss: 1.7423 - acc: 0.3438 - val_loss: 1.1643 - val_acc: 0.6115\n", 194 | "Epoch 4/15\n", 195 | "857/857 [==============================] - 30s 35ms/step - loss: 1.4933 - acc: 0.4688 - val_loss: 1.1572 - val_acc: 0.6064\n", 196 | "Epoch 5/15\n", 197 | "857/857 [==============================] - 22s 26ms/step - loss: 1.3521 - acc: 0.7500 - val_loss: 0.8973 - val_acc: 0.7167\n", 198 | "Epoch 6/15\n", 199 | "857/857 [==============================] - 21s 24ms/step - loss: 1.2332 - acc: 0.5625 - val_loss: 0.8082 - val_acc: 0.7200\n", 200 | "Epoch 7/15\n", 201 | "857/857 [==============================] - 44s 51ms/step - loss: 1.1631 - acc: 0.5938 - val_loss: 0.8671 - val_acc: 0.7352\n", 202 | "Epoch 8/15\n", 203 | "857/857 [==============================] - 58s 67ms/step - loss: 1.0857 - acc: 0.7188 - val_loss: 0.7608 - val_acc: 0.7949\n", 204 | "Epoch 9/15\n", 205 | "857/857 [==============================] - 23s 26ms/step - loss: 1.0197 - acc: 0.6562 - val_loss: 0.6978 - val_acc: 0.7674\n", 206 | "Epoch 10/15\n", 207 | "857/857 [==============================] - 28s 33ms/step - loss: 0.9711 - acc: 0.6875 - val_loss: 0.7027 - val_acc: 0.7984\n", 208 | "Epoch 11/15\n", 209 | "857/857 [==============================] - 53s 61ms/step - loss: 0.9076 - acc: 0.5625 - val_loss: 0.5784 - val_acc: 0.8238\n", 210 | "Epoch 12/15\n", 211 | "857/857 [==============================] - 64s 75ms/step - loss: 0.8764 - acc: 0.5938 - val_loss: 0.6079 - val_acc: 0.8133\n", 212 | "Epoch 13/15\n", 213 | "857/857 [==============================] - 28s 33ms/step - loss: 0.8410 - acc: 0.7500 - val_loss: 0.4547 - val_acc: 0.8182\n", 214 | "Epoch 14/15\n", 215 | "857/857 [==============================] - 22s 26ms/step - loss: 0.8045 - acc: 0.5312 - val_loss: 0.2415 - val_acc: 0.8496\n", 216 | "Epoch 15/15\n", 217 | "857/857 [==============================] - 47s 55ms/step - loss: 0.7836 - acc: 0.7188 - val_loss: 0.3857 - val_acc: 0.8425\n", 218 | "7172/7172 [==============================] - 4s 596us/step - loss: 6.9243 - acc: 0.5661\n" 219 | ] 220 | }, 221 | { 222 | "data": { 223 | "text/plain": [ 224 | "[6.92426086682151, 0.56609035]" 225 | ] 226 | }, 227 | "execution_count": 5, 228 | "metadata": { 229 | "tags": [] 230 | }, 231 | "output_type": "execute_result" 232 | } 233 | ], 234 | "source": [ 235 | "model = tf.keras.models.Sequential([\n", 236 | " tf.keras.layers.Conv2D(64, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n", 237 | " tf.keras.layers.MaxPooling2D(2, 2),\n", 238 | " tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n", 239 | " tf.keras.layers.MaxPooling2D(2, 2),\n", 240 | " tf.keras.layers.Flatten(),\n", 241 | " tf.keras.layers.Dense(128, activation=tf.nn.relu),\n", 242 | " tf.keras.layers.Dense(26, activation=tf.nn.softmax)])\n", 243 | "\n", 244 | "model.compile(optimizer = tf.train.AdamOptimizer(),\n", 245 | " loss = 'sparse_categorical_crossentropy',\n", 246 | " metrics=['accuracy'])\n", 247 | "\n", 248 | "history = model.fit_generator(train_datagen.flow(training_images, training_labels, batch_size=32),\n", 249 | " steps_per_epoch=len(training_images) / 32,\n", 250 | " epochs=15,\n", 251 | " validation_data=validation_datagen.flow(testing_images, testing_labels, batch_size=32),\n", 252 | " validation_steps=len(testing_images) / 32)\n", 253 | "\n", 254 | "model.evaluate(testing_images, testing_labels)\n" 255 | ] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "execution_count": 0, 260 | "metadata": { 261 | "colab": { 262 | "height": 561 263 | }, 264 | "colab_type": "code", 265 | "id": "_Q3Zpr46dsij", 266 | "outputId": "fd4001fb-9a21-4192-dbdb-9a2f2174e769" 267 | }, 268 | "outputs": [ 269 | { 270 | "data": { 271 | "image/png": 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NePPNt1m4cAE6nQ6ZTMb//d9iWrduw8svv8a8ebPx8fEhJKTHfbWVb5vq7nvh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\nHHr3rvWYtuekKRRfukTamq9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6dBEEAevWbfCc8hQOPXvh3crHfHvODwBSSznez8zk1gfv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\niY5DRG7GpfLPyMjA0qVL8cknn2DJkiVYs2ZNl+v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BAXffPYkJEybTps313RrExcWycuV/kGWZmTPv49ixI/Tq1ZuVK19lwIBB/OUv00lNTWHG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\n4ejRo1i6dGnY54t0OtLd3T3r1EUNgsEg3r59q8r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284 | "text/plain": [ 285 | "" 286 | ] 287 | }, 288 | "metadata": { 289 | "tags": [] 290 | }, 291 | "output_type": "display_data" 292 | } 293 | ], 294 | "source": [ 295 | "import matplotlib.pyplot as plt\n", 296 | "acc = history.history['acc']\n", 297 | "val_acc = history.history['val_acc']\n", 298 | "loss = history.history['loss']\n", 299 | "val_loss = history.history['val_loss']\n", 300 | "\n", 301 | "epochs = range(len(acc))\n", 302 | "\n", 303 | "plt.plot(epochs, acc, 'r', label='Training accuracy')\n", 304 | "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n", 305 | "plt.title('Training and validation accuracy')\n", 306 | "plt.legend()\n", 307 | "plt.figure()\n", 308 | "\n", 309 | "plt.plot(epochs, loss, 'r', label='Training Loss')\n", 310 | "plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n", 311 | "plt.title('Training and validation loss')\n", 312 | "plt.legend()\n", 313 | "\n", 314 | "plt.show()" 315 | ] 316 | } 317 | ], 318 | "metadata": { 319 | "colab": { 320 | "name": "Exercise 8 - Answer.ipynb", 321 | "provenance": [] 322 | }, 323 | "kernelspec": { 324 | "display_name": "Python 3", 325 | "language": "python", 326 | "name": "python3" 327 | }, 328 | "language_info": { 329 | "codemirror_mode": { 330 | "name": "ipython", 331 | "version": 3 332 | }, 333 | "file_extension": ".py", 334 | "mimetype": "text/x-python", 335 | "name": "python", 336 | "nbconvert_exporter": "python", 337 | "pygments_lexer": "ipython3", 338 | "version": "3.5.6" 339 | } 340 | }, 341 | "nbformat": 4, 342 | "nbformat_minor": 1 343 | } 344 | -------------------------------------------------------------------------------- /2 - Convolutional Neural Networks in TensorFlow/Week 4/Week 4 Quiz.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/2 - Convolutional Neural Networks in TensorFlow/Week 4/Week 4 Quiz.pdf -------------------------------------------------------------------------------- /2 - Convolutional Neural Networks in TensorFlow/Week 4/Week 4 Quiz.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/2 - Convolutional Neural Networks in TensorFlow/Week 4/Week 4 Quiz.png -------------------------------------------------------------------------------- /3 - Natural Langu age Processing in TensorFlow/Natural Langu age Processing in TensorFlow.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/3 - Natural Langu age Processing in TensorFlow/Natural Langu age Processing in TensorFlow.pdf -------------------------------------------------------------------------------- /3 - Natural Langu age Processing in TensorFlow/Week 1/Course 3 Week 1.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Exercise-answer.ipynb", 7 | "provenance": [] 8 | }, 9 | "kernelspec": { 10 | "name": "python3", 11 | "display_name": "Python 3" 12 | } 13 | }, 14 | "cells": [ 15 | { 16 | "cell_type": "code", 17 | "metadata": { 18 | "id": "zrZevCPJ92HG", 19 | "colab_type": "code", 20 | "colab": {} 21 | }, 22 | "source": [ 23 | "!wget --no-check-certificate \\\n", 24 | " https://storage.googleapis.com/laurencemoroney-blog.appspot.com/bbc-text.csv \\\n", 25 | " -O /tmp/bbc-text.csv\n", 26 | "\n", 27 | " \n", 28 | "import csv\n", 29 | "from tensorflow.keras.preprocessing.text import Tokenizer\n", 30 | "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", 31 | "\n", 32 | "\n", 33 | "\n", 34 | "#Stopwords list from https://github.com/Yoast/YoastSEO.js/blob/develop/src/config/stopwords.js\n", 35 | "stopwords = [ \"a\", \"about\", \"above\", \"after\", \"again\", \"against\", \"all\", \"am\", \"an\", \"and\", \"any\", \"are\", \"as\", \"at\", \"be\", \"because\", \"been\", \"before\", \"being\", \"below\", \"between\", \"both\", \"but\", \"by\", \"could\", \"did\", \"do\", \"does\", \"doing\", \"down\", \"during\", \"each\", \"few\", \"for\", \"from\", \"further\", \"had\", \"has\", \"have\", \"having\", \"he\", \"he'd\", \"he'll\", \"he's\", \"her\", \"here\", \"here's\", \"hers\", \"herself\", \"him\", \"himself\", \"his\", \"how\", \"how's\", \"i\", \"i'd\", \"i'll\", \"i'm\", \"i've\", \"if\", \"in\", \"into\", \"is\", \"it\", \"it's\", \"its\", \"itself\", \"let's\", \"me\", \"more\", \"most\", \"my\", \"myself\", \"nor\", \"of\", \"on\", \"once\", \"only\", \"or\", \"other\", \"ought\", \"our\", \"ours\", \"ourselves\", \"out\", \"over\", \"own\", \"same\", \"she\", \"she'd\", \"she'll\", \"she's\", \"should\", \"so\", \"some\", \"such\", \"than\", \"that\", \"that's\", \"the\", \"their\", \"theirs\", \"them\", \"themselves\", \"then\", \"there\", \"there's\", \"these\", \"they\", \"they'd\", \"they'll\", \"they're\", \"they've\", \"this\", \"those\", \"through\", \"to\", \"too\", \"under\", \"until\", \"up\", \"very\", \"was\", \"we\", \"we'd\", \"we'll\", \"we're\", \"we've\", \"were\", \"what\", \"what's\", \"when\", \"when's\", \"where\", \"where's\", \"which\", \"while\", \"who\", \"who's\", \"whom\", \"why\", \"why's\", \"with\", \"would\", \"you\", \"you'd\", \"you'll\", \"you're\", \"you've\", \"your\", \"yours\", \"yourself\", \"yourselves\" ]\n" 36 | ], 37 | "execution_count": 0, 38 | "outputs": [] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "metadata": { 43 | "id": "1rmYBjsyCv3K", 44 | "colab_type": "code", 45 | "colab": {} 46 | }, 47 | "source": [ 48 | "sentences = []\n", 49 | "labels = []\n", 50 | "with open(\"/tmp/bbc-text.csv\", 'r') as csvfile:\n", 51 | " reader = csv.reader(csvfile, delimiter=',')\n", 52 | " next(reader)\n", 53 | " for row in reader:\n", 54 | " labels.append(row[0])\n", 55 | " sentence = row[1]\n", 56 | " for word in stopwords:\n", 57 | " token = \" \" + word + \" \"\n", 58 | " sentence = sentence.replace(token, \" \")\n", 59 | " sentence = sentence.replace(\" \", \" \")\n", 60 | " sentences.append(sentence)\n", 61 | "\n", 62 | "\n", 63 | "print(len(sentences))\n", 64 | "print(sentences[0])\n", 65 | "\n", 66 | "#Expected output\n", 67 | "# 2225\n", 68 | "# tv future hands viewers home theatre systems plasma high-definition tvs digital video recorders moving living room way people watch tv will radically different five years time. according expert panel gathered annual consumer electronics show las vegas discuss new technologies will impact one favourite pastimes. us leading trend programmes content will delivered viewers via home networks cable satellite telecoms companies broadband service providers front rooms portable devices. one talked-about technologies ces digital personal video recorders (dvr pvr). set-top boxes like us s tivo uk s sky+ system allow people record store play pause forward wind tv programmes want. essentially technology allows much personalised tv. also built-in high-definition tv sets big business japan us slower take off europe lack high-definition programming. not can people forward wind adverts can also forget abiding network channel schedules putting together a-la-carte entertainment. us networks cable satellite companies worried means terms advertising revenues well brand identity viewer loyalty channels. although us leads technology moment also concern raised europe particularly growing uptake services like sky+. happens today will see nine months years time uk adam hume bbc broadcast s futurologist told bbc news website. likes bbc no issues lost advertising revenue yet. pressing issue moment commercial uk broadcasters brand loyalty important everyone. will talking content brands rather network brands said tim hanlon brand communications firm starcom mediavest. reality broadband connections anybody can producer content. added: challenge now hard promote programme much choice. means said stacey jolna senior vice president tv guide tv group way people find content want watch simplified tv viewers. means networks us terms channels take leaf google s book search engine future instead scheduler help people find want watch. kind channel model might work younger ipod generation used taking control gadgets play them. might not suit everyone panel recognised. older generations comfortable familiar schedules channel brands know getting. perhaps not want much choice put hands mr hanlon suggested. end kids just diapers pushing buttons already - everything possible available said mr hanlon. ultimately consumer will tell market want. 50 000 new gadgets technologies showcased ces many enhancing tv-watching experience. high-definition tv sets everywhere many new models lcd (liquid crystal display) tvs launched dvr capability built instead external boxes. one example launched show humax s 26-inch lcd tv 80-hour tivo dvr dvd recorder. one us s biggest satellite tv companies directtv even launched branded dvr show 100-hours recording capability instant replay search function. set can pause rewind tv 90 hours. microsoft chief bill gates announced pre-show keynote speech partnership tivo called tivotogo means people can play recorded programmes windows pcs mobile devices. reflect increasing trend freeing multimedia people can watch want want." 69 | ], 70 | "execution_count": 0, 71 | "outputs": [] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "metadata": { 76 | "id": "9LhzBBgSC3S5", 77 | "colab_type": "code", 78 | "colab": {} 79 | }, 80 | "source": [ 81 | "tokenizer = Tokenizer(oov_token=\"\")\n", 82 | "tokenizer.fit_on_texts(sentences)\n", 83 | "word_index = tokenizer.word_index\n", 84 | "print(len(word_index))\n", 85 | "# Expected output\n", 86 | "# 29714" 87 | ], 88 | "execution_count": 0, 89 | "outputs": [] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "metadata": { 94 | "id": "1Gr3dbQfC5VR", 95 | "colab_type": "code", 96 | "colab": {} 97 | }, 98 | "source": [ 99 | "sequences = tokenizer.texts_to_sequences(sentences)\n", 100 | "padded = pad_sequences(sequences, padding='post')\n", 101 | "print(padded[0])\n", 102 | "print(padded.shape)\n", 103 | "\n", 104 | "# Expected output\n", 105 | "# [ 96 176 1158 ... 0 0 0]\n", 106 | "# (2225, 2442)" 107 | ], 108 | "execution_count": 0, 109 | "outputs": [] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "metadata": { 114 | "id": "fZufOahzC6yx", 115 | "colab_type": "code", 116 | "colab": {} 117 | }, 118 | "source": [ 119 | "label_tokenizer = Tokenizer()\n", 120 | "label_tokenizer.fit_on_texts(labels)\n", 121 | "label_word_index = label_tokenizer.word_index\n", 122 | "label_seq = label_tokenizer.texts_to_sequences(labels)\n", 123 | "print(label_seq)\n", 124 | "print(label_word_index)\n", 125 | "# Expected Output\n", 126 | "# [[4], [2], [1], [1], [5], [3], [3], [1], [1], [5], [5], [2], [2], [3], [1], [2], [3], [1], [2], [4], [4], [4], [1], [1], [4], [1], [5], [4], [3], [5], [3], [4], [5], [5], [2], [3], [4], [5], [3], [2], [3], [1], [2], [1], [4], [5], [3], [3], [3], [2], [1], [3], [2], [2], [1], [3], [2], [1], [1], [2], [2], [1], [2], [1], [2], [4], [2], [5], [4], [2], [3], [2], [3], [1], [2], [4], [2], [1], [1], [2], [2], [1], [3], [2], [5], [3], [3], [2], [5], [2], [1], [1], [3], [1], [3], [1], [2], [1], [2], [5], [5], [1], [2], [3], [3], [4], [1], [5], [1], [4], [2], [5], [1], [5], [1], [5], [5], [3], [1], [1], [5], [3], [2], [4], [2], [2], [4], [1], [3], [1], [4], [5], [1], [2], [2], [4], [5], [4], [1], [2], [2], [2], [4], [1], [4], [2], [1], [5], [1], [4], [1], [4], [3], [2], [4], [5], [1], [2], [3], [2], [5], [3], [3], [5], [3], [2], [5], [3], [3], [5], 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[4], [1], [1], [4], [4], [3], [2], [1], [5], [5], [1], [5], [4], [4], [2], [2], [2], [1], [1], [4], [1], [2], [4], [2], [2], [1], [2], [3], [2], [2], [4], [2], [4], [3], [4], [5], [3], [4], [5], [1], [3], [5], [2], [4], [2], [4], [5], [4], [1], [2], [2], [3], [5], [3], [1]]\n", 127 | "# {'sport': 1, 'business': 2, 'politics': 3, 'tech': 4, 'entertainment': 5}" 128 | ], 129 | "execution_count": 0, 130 | "outputs": [] 131 | } 132 | ] 133 | } -------------------------------------------------------------------------------- /3 - Natural Langu age Processing in TensorFlow/Week 1/Week 1 Quiz.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/3 - Natural Langu age Processing in TensorFlow/Week 1/Week 1 Quiz.pdf -------------------------------------------------------------------------------- /3 - Natural Langu age Processing in TensorFlow/Week 1/Week 1 Quiz.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/3 - Natural Langu age Processing in TensorFlow/Week 1/Week 1 Quiz.png -------------------------------------------------------------------------------- /3 - Natural Langu age Processing in TensorFlow/Week 2/Course 3 Week 2.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Course 3 - Week 2 - Exercise - Answer.ipynb", 7 | "provenance": [], 8 | "collapsed_sections": [] 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "accelerator": "GPU" 15 | }, 16 | "cells": [ 17 | { 18 | "cell_type": "code", 19 | "metadata": { 20 | "id": "gnwiOnGyW5JK", 21 | "colab_type": "code", 22 | "colab": {} 23 | }, 24 | "source": [ 25 | "import csv\n", 26 | "import tensorflow as tf\n", 27 | "import numpy as np\n", 28 | "from tensorflow.keras.preprocessing.text import Tokenizer\n", 29 | "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", 30 | "\n", 31 | "!wget --no-check-certificate \\\n", 32 | " https://storage.googleapis.com/laurencemoroney-blog.appspot.com/bbc-text.csv \\\n", 33 | " -O /tmp/bbc-text.csv" 34 | ], 35 | "execution_count": 0, 36 | "outputs": [] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "metadata": { 41 | "id": "EYo6A4v5ZABQ", 42 | "colab_type": "code", 43 | "colab": {} 44 | }, 45 | "source": [ 46 | "vocab_size = 1000\n", 47 | "embedding_dim = 16\n", 48 | "max_length = 120\n", 49 | "trunc_type='post'\n", 50 | "padding_type='post'\n", 51 | "oov_tok = \"\"\n", 52 | "training_portion = .8" 53 | ], 54 | "execution_count": 0, 55 | "outputs": [] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "metadata": { 60 | "id": "iU1qq3_SZBx_", 61 | "colab_type": "code", 62 | "colab": {} 63 | }, 64 | "source": [ 65 | "sentences = []\n", 66 | "labels = []\n", 67 | "stopwords = [ \"a\", \"about\", \"above\", \"after\", \"again\", \"against\", \"all\", \"am\", \"an\", \"and\", \"any\", \"are\", \"as\", \"at\", \"be\", \"because\", \"been\", \"before\", \"being\", \"below\", \"between\", \"both\", \"but\", \"by\", \"could\", \"did\", \"do\", \"does\", \"doing\", \"down\", \"during\", \"each\", \"few\", \"for\", \"from\", \"further\", \"had\", \"has\", \"have\", \"having\", \"he\", \"he'd\", \"he'll\", \"he's\", \"her\", \"here\", \"here's\", \"hers\", \"herself\", \"him\", \"himself\", \"his\", \"how\", \"how's\", \"i\", \"i'd\", \"i'll\", \"i'm\", \"i've\", \"if\", \"in\", \"into\", \"is\", \"it\", \"it's\", \"its\", \"itself\", \"let's\", \"me\", \"more\", \"most\", \"my\", \"myself\", \"nor\", \"of\", \"on\", \"once\", \"only\", \"or\", \"other\", \"ought\", \"our\", \"ours\", \"ourselves\", \"out\", \"over\", \"own\", \"same\", \"she\", \"she'd\", \"she'll\", \"she's\", \"should\", \"so\", \"some\", \"such\", \"than\", \"that\", \"that's\", \"the\", \"their\", \"theirs\", \"them\", \"themselves\", \"then\", \"there\", \"there's\", \"these\", \"they\", \"they'd\", \"they'll\", \"they're\", \"they've\", \"this\", \"those\", \"through\", \"to\", \"too\", \"under\", \"until\", \"up\", \"very\", \"was\", \"we\", \"we'd\", \"we'll\", \"we're\", \"we've\", \"were\", \"what\", \"what's\", \"when\", \"when's\", \"where\", \"where's\", \"which\", \"while\", \"who\", \"who's\", \"whom\", \"why\", \"why's\", \"with\", \"would\", \"you\", \"you'd\", \"you'll\", \"you're\", \"you've\", \"your\", \"yours\", \"yourself\", \"yourselves\" ]\n", 68 | "print(len(stopwords))\n", 69 | "# Expected Output\n", 70 | "# 153" 71 | ], 72 | "execution_count": 0, 73 | "outputs": [] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "metadata": { 78 | "id": "eutB2xMiZD0e", 79 | "colab_type": "code", 80 | "colab": {} 81 | }, 82 | "source": [ 83 | "with open(\"/tmp/bbc-text.csv\", 'r') as csvfile:\n", 84 | " reader = csv.reader(csvfile, delimiter=',')\n", 85 | " next(reader)\n", 86 | " for row in reader:\n", 87 | " labels.append(row[0])\n", 88 | " sentence = row[1]\n", 89 | " for word in stopwords:\n", 90 | " token = \" \" + word + \" \"\n", 91 | " sentence = sentence.replace(token, \" \")\n", 92 | " sentences.append(sentence)\n", 93 | "\n", 94 | "print(len(labels))\n", 95 | "print(len(sentences))\n", 96 | "print(sentences[0])\n", 97 | "# Expected Output\n", 98 | "# 2225\n", 99 | "# 2225\n", 100 | "# tv future hands viewers home theatre systems plasma high-definition tvs digital video recorders moving living room way people watch tv will radically different five years time. according expert panel gathered annual consumer electronics show las vegas discuss new technologies will impact one favourite pastimes. us leading trend programmes content will delivered viewers via home networks cable satellite telecoms companies broadband service providers front rooms portable devices. one talked-about technologies ces digital personal video recorders (dvr pvr). set-top boxes like us s tivo uk s sky+ system allow people record store play pause forward wind tv programmes want. essentially technology allows much personalised tv. also built-in high-definition tv sets big business japan us slower take off europe lack high-definition programming. not can people forward wind adverts can also forget abiding network channel schedules putting together a-la-carte entertainment. us networks cable satellite companies worried means terms advertising revenues well brand identity viewer loyalty channels. although us leads technology moment also concern raised europe particularly growing uptake services like sky+. happens today will see nine months years time uk adam hume bbc broadcast s futurologist told bbc news website. likes bbc no issues lost advertising revenue yet. pressing issue moment commercial uk broadcasters brand loyalty important everyone. will talking content brands rather network brands said tim hanlon brand communications firm starcom mediavest. reality broadband connections anybody can producer content. added: challenge now hard promote programme much choice. means said stacey jolna senior vice president tv guide tv group way people find content want watch simplified tv viewers. means networks us terms channels take leaf google s book search engine future instead scheduler help people find want watch. kind channel model might work younger ipod generation used taking control gadgets play them. might not suit everyone panel recognised. older generations comfortable familiar schedules channel brands know getting. perhaps not want much choice put hands mr hanlon suggested. end kids just diapers pushing buttons already - everything possible available said mr hanlon. ultimately consumer will tell market want. 50 000 new gadgets technologies showcased ces many enhancing tv-watching experience. high-definition tv sets everywhere many new models lcd (liquid crystal display) tvs launched dvr capability built instead external boxes. one example launched show humax s 26-inch lcd tv 80-hour tivo dvr dvd recorder. one us s biggest satellite tv companies directtv even launched branded dvr show 100-hours recording capability instant replay search function. set can pause rewind tv 90 hours. microsoft chief bill gates announced pre-show keynote speech partnership tivo called tivotogo means people can play recorded programmes windows pcs mobile devices. reflect increasing trend freeing multimedia people can watch want want." 101 | ], 102 | "execution_count": 0, 103 | "outputs": [] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "metadata": { 108 | "id": "XfdaWh06ZGe3", 109 | "colab_type": "code", 110 | "colab": {} 111 | }, 112 | "source": [ 113 | "train_size = int(len(sentences) * training_portion)\n", 114 | "\n", 115 | "train_sentences = sentences[:train_size]\n", 116 | "train_labels = labels[:train_size]\n", 117 | "\n", 118 | "validation_sentences = sentences[train_size:]\n", 119 | "validation_labels = labels[train_size:]\n", 120 | "\n", 121 | "print(train_size)\n", 122 | "print(len(train_sentences))\n", 123 | "print(len(train_labels))\n", 124 | "print(len(validation_sentences))\n", 125 | "print(len(validation_labels))\n", 126 | "\n", 127 | "# Expected output (if training_portion=.8)\n", 128 | "# 1780\n", 129 | "# 1780\n", 130 | "# 1780\n", 131 | "# 445\n", 132 | "# 445" 133 | ], 134 | "execution_count": 0, 135 | "outputs": [] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "metadata": { 140 | "id": "ULzA8xhwZI22", 141 | "colab_type": "code", 142 | "colab": {} 143 | }, 144 | "source": [ 145 | "tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)\n", 146 | "tokenizer.fit_on_texts(train_sentences)\n", 147 | "word_index = tokenizer.word_index\n", 148 | "\n", 149 | "train_sequences = tokenizer.texts_to_sequences(train_sentences)\n", 150 | "train_padded = pad_sequences(train_sequences, padding=padding_type, maxlen=max_length)\n", 151 | "\n", 152 | "print(len(train_sequences[0]))\n", 153 | "print(len(train_padded[0]))\n", 154 | "\n", 155 | "print(len(train_sequences[1]))\n", 156 | "print(len(train_padded[1]))\n", 157 | "\n", 158 | "print(len(train_sequences[10]))\n", 159 | "print(len(train_padded[10]))\n", 160 | "\n", 161 | "# Expected Ouput\n", 162 | "# 449\n", 163 | "# 120\n", 164 | "# 200\n", 165 | "# 120\n", 166 | "# 192\n", 167 | "# 120" 168 | ], 169 | "execution_count": 0, 170 | "outputs": [] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "metadata": { 175 | "id": "c8PeFWzPZLW_", 176 | "colab_type": "code", 177 | "colab": {} 178 | }, 179 | "source": [ 180 | "validation_sequences = tokenizer.texts_to_sequences(validation_sentences)\n", 181 | "validation_padded = pad_sequences(validation_sequences, padding=padding_type, maxlen=max_length)\n", 182 | "\n", 183 | "print(len(validation_sequences))\n", 184 | "print(validation_padded.shape)\n", 185 | "\n", 186 | "# Expected output\n", 187 | "# 445\n", 188 | "# (445, 120)" 189 | ], 190 | "execution_count": 0, 191 | "outputs": [] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "metadata": { 196 | "id": "XkWiQ_FKZNp2", 197 | "colab_type": "code", 198 | "colab": {} 199 | }, 200 | "source": [ 201 | "label_tokenizer = Tokenizer()\n", 202 | "label_tokenizer.fit_on_texts(labels)\n", 203 | "\n", 204 | "training_label_seq = np.array(label_tokenizer.texts_to_sequences(train_labels))\n", 205 | "validation_label_seq = np.array(label_tokenizer.texts_to_sequences(validation_labels))\n", 206 | "\n", 207 | "print(training_label_seq[0])\n", 208 | "print(training_label_seq[1])\n", 209 | "print(training_label_seq[2])\n", 210 | "print(training_label_seq.shape)\n", 211 | "\n", 212 | "print(validation_label_seq[0])\n", 213 | "print(validation_label_seq[1])\n", 214 | "print(validation_label_seq[2])\n", 215 | "print(validation_label_seq.shape)\n", 216 | "\n", 217 | "# Expected output\n", 218 | "# [4]\n", 219 | "# [2]\n", 220 | "# [1]\n", 221 | "# (1780, 1)\n", 222 | "# [5]\n", 223 | "# [4]\n", 224 | "# [3]\n", 225 | "# (445, 1)" 226 | ], 227 | "execution_count": 0, 228 | "outputs": [] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "metadata": { 233 | "id": "HZ5um4MWZP-W", 234 | "colab_type": "code", 235 | "colab": {} 236 | }, 237 | "source": [ 238 | "model = tf.keras.Sequential([\n", 239 | " tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n", 240 | " tf.keras.layers.GlobalAveragePooling1D(),\n", 241 | " tf.keras.layers.Dense(24, activation='relu'),\n", 242 | " tf.keras.layers.Dense(6, activation='softmax')\n", 243 | "])\n", 244 | "model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n", 245 | "model.summary()\n", 246 | "\n", 247 | "# Expected Output\n", 248 | "# Layer (type) Output Shape Param # \n", 249 | "# =================================================================\n", 250 | "# embedding (Embedding) (None, 120, 16) 16000 \n", 251 | "# _________________________________________________________________\n", 252 | "# global_average_pooling1d (Gl (None, 16) 0 \n", 253 | "# _________________________________________________________________\n", 254 | "# dense (Dense) (None, 24) 408 \n", 255 | "# _________________________________________________________________\n", 256 | "# dense_1 (Dense) (None, 6) 150 \n", 257 | "# =================================================================\n", 258 | "# Total params: 16,558\n", 259 | "# Trainable params: 16,558\n", 260 | "# Non-trainable params: 0" 261 | ], 262 | "execution_count": 0, 263 | "outputs": [] 264 | }, 265 | { 266 | "cell_type": "code", 267 | "metadata": { 268 | "id": "XsfdxySKZSXu", 269 | "colab_type": "code", 270 | "colab": {} 271 | }, 272 | "source": [ 273 | "num_epochs = 30\n", 274 | "history = model.fit(train_padded, training_label_seq, epochs=num_epochs, validation_data=(validation_padded, validation_label_seq), verbose=2)" 275 | ], 276 | "execution_count": 0, 277 | "outputs": [] 278 | }, 279 | { 280 | "cell_type": "code", 281 | "metadata": { 282 | "id": "dQ0BX2apXS9u", 283 | "colab_type": "code", 284 | "colab": {} 285 | }, 286 | "source": [ 287 | "import matplotlib.pyplot as plt\n", 288 | "\n", 289 | "\n", 290 | "def plot_graphs(history, string):\n", 291 | " plt.plot(history.history[string])\n", 292 | " plt.plot(history.history['val_'+string])\n", 293 | " plt.xlabel(\"Epochs\")\n", 294 | " plt.ylabel(string)\n", 295 | " plt.legend([string, 'val_'+string])\n", 296 | " plt.show()\n", 297 | " \n", 298 | "plot_graphs(history, \"acc\")\n", 299 | "plot_graphs(history, \"loss\")" 300 | ], 301 | "execution_count": 0, 302 | "outputs": [] 303 | }, 304 | { 305 | "cell_type": "code", 306 | "metadata": { 307 | "id": "w7Xc-uWxXhML", 308 | "colab_type": "code", 309 | "colab": {} 310 | }, 311 | "source": [ 312 | "\n", 313 | "\n", 314 | "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n", 315 | "\n", 316 | "def decode_sentence(text):\n", 317 | " return ' '.join([reverse_word_index.get(i, '?') for i in text])\n" 318 | ], 319 | "execution_count": 0, 320 | "outputs": [] 321 | }, 322 | { 323 | "cell_type": "code", 324 | "metadata": { 325 | "id": "OhnFA_TDXrih", 326 | "colab_type": "code", 327 | "colab": {} 328 | }, 329 | "source": [ 330 | "e = model.layers[0]\n", 331 | "weights = e.get_weights()[0]\n", 332 | "print(weights.shape) # shape: (vocab_size, embedding_dim)\n", 333 | "\n", 334 | "# Expected output\n", 335 | "# (1000, 16)" 336 | ], 337 | "execution_count": 0, 338 | "outputs": [] 339 | }, 340 | { 341 | "cell_type": "code", 342 | "metadata": { 343 | "id": "_POzcWWAXudL", 344 | "colab_type": "code", 345 | "colab": {} 346 | }, 347 | "source": [ 348 | "import io\n", 349 | "\n", 350 | "out_v = io.open('vecs.tsv', 'w', encoding='utf-8')\n", 351 | "out_m = io.open('meta.tsv', 'w', encoding='utf-8')\n", 352 | "for word_num in range(1, vocab_size):\n", 353 | " word = reverse_word_index[word_num]\n", 354 | " embeddings = weights[word_num]\n", 355 | " out_m.write(word + \"\\n\")\n", 356 | " out_v.write('\\t'.join([str(x) for x in embeddings]) + \"\\n\")\n", 357 | "out_v.close()\n", 358 | "out_m.close()" 359 | ], 360 | "execution_count": 0, 361 | "outputs": [] 362 | }, 363 | { 364 | "cell_type": "code", 365 | "metadata": { 366 | "id": "VmqpQMZ_XyOa", 367 | "colab_type": "code", 368 | "colab": {} 369 | }, 370 | "source": [ 371 | "try:\n", 372 | " from google.colab import files\n", 373 | "except ImportError:\n", 374 | " pass\n", 375 | "else:\n", 376 | " files.download('vecs.tsv')\n", 377 | " files.download('meta.tsv')" 378 | ], 379 | "execution_count": 0, 380 | "outputs": [] 381 | } 382 | ] 383 | } -------------------------------------------------------------------------------- /3 - Natural Langu age Processing in TensorFlow/Week 2/Week 2 Quiz.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/3 - Natural Langu age Processing in TensorFlow/Week 2/Week 2 Quiz.pdf -------------------------------------------------------------------------------- /3 - Natural Langu age Processing in TensorFlow/Week 2/Week 2 Quiz.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/3 - Natural Langu age Processing in TensorFlow/Week 2/Week 2 Quiz.png -------------------------------------------------------------------------------- /3 - Natural Langu age Processing in TensorFlow/Week 3/Course 3 Week 3.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "NLP Course - Week 3 Exercise Answer.ipynb", 7 | "provenance": [] 8 | }, 9 | "kernelspec": { 10 | "name": "python3", 11 | "display_name": "Python 3" 12 | }, 13 | "accelerator": "GPU" 14 | }, 15 | "cells": [ 16 | { 17 | "cell_type": "code", 18 | "metadata": { 19 | "id": "hmA6EzkQJ5jt", 20 | "colab_type": "code", 21 | "colab": {} 22 | }, 23 | "source": [ 24 | "import json\n", 25 | "import tensorflow as tf\n", 26 | "import csv\n", 27 | "import random\n", 28 | "import numpy as np\n", 29 | "\n", 30 | "from tensorflow.keras.preprocessing.text import Tokenizer\n", 31 | "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", 32 | "from tensorflow.keras.utils import to_categorical\n", 33 | "from tensorflow.keras import regularizers\n", 34 | "\n", 35 | "\n", 36 | "embedding_dim = 100\n", 37 | "max_length = 16\n", 38 | "trunc_type='post'\n", 39 | "padding_type='post'\n", 40 | "oov_tok = \"\"\n", 41 | "training_size=160000\n", 42 | "test_portion=.1\n", 43 | "\n", 44 | "corpus = []\n" 45 | ], 46 | "execution_count": 0, 47 | "outputs": [] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "metadata": { 52 | "id": "bM0l_dORKqE0", 53 | "colab_type": "code", 54 | "outputId": "491ba86b-f780-4355-a4be-765565a29c8c", 55 | "colab": { 56 | "base_uri": "https://localhost:8080/", 57 | "height": 204 58 | } 59 | }, 60 | "source": [ 61 | "\n", 62 | "# Note that I cleaned the Stanford dataset to remove LATIN1 encoding to make it easier for Python CSV reader\n", 63 | "# You can do that yourself with:\n", 64 | "# iconv -f LATIN1 -t UTF8 training.1600000.processed.noemoticon.csv -o training_cleaned.csv\n", 65 | "# I then hosted it on my site to make it easier to use in this notebook\n", 66 | "\n", 67 | "!wget --no-check-certificate \\\n", 68 | " https://storage.googleapis.com/laurencemoroney-blog.appspot.com/training_cleaned.csv \\\n", 69 | " -O /tmp/training_cleaned.csv\n", 70 | "\n", 71 | "num_sentences = 0\n", 72 | "\n", 73 | "with open(\"/tmp/training_cleaned.csv\") as csvfile:\n", 74 | " reader = csv.reader(csvfile, delimiter=',')\n", 75 | " for row in reader:\n", 76 | " list_item=[]\n", 77 | " list_item.append(row[5])\n", 78 | " this_label=row[0]\n", 79 | " if this_label=='0':\n", 80 | " list_item.append(0)\n", 81 | " else:\n", 82 | " list_item.append(1)\n", 83 | " num_sentences = num_sentences + 1\n", 84 | " corpus.append(list_item)\n", 85 | "\n", 86 | "\n" 87 | ], 88 | "execution_count": 0, 89 | "outputs": [ 90 | { 91 | "output_type": "stream", 92 | "text": [ 93 | "--2019-06-07 17:53:35-- https://storage.googleapis.com/laurencemoroney-blog.appspot.com/training_cleaned.csv\n", 94 | "Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.192.128, 2607:f8b0:4001:c1d::80\n", 95 | "Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.192.128|:443... connected.\n", 96 | "HTTP request sent, awaiting response... 200 OK\n", 97 | "Length: 238942690 (228M) [application/octet-stream]\n", 98 | "Saving to: ‘/tmp/training_cleaned.csv’\n", 99 | "\n", 100 | "/tmp/training_clean 100%[===================>] 227.87M 221MB/s in 1.0s \n", 101 | "\n", 102 | "2019-06-07 17:53:36 (221 MB/s) - ‘/tmp/training_cleaned.csv’ saved [238942690/238942690]\n", 103 | "\n" 104 | ], 105 | "name": "stdout" 106 | } 107 | ] 108 | }, 109 | { 110 | "cell_type": "code", 111 | "metadata": { 112 | "id": "3kxblBUjEUX-", 113 | "colab_type": "code", 114 | "outputId": "3c0227a2-e74b-4d9b-cabb-f9ee150571b1", 115 | "colab": { 116 | "base_uri": "https://localhost:8080/", 117 | "height": 68 118 | } 119 | }, 120 | "source": [ 121 | "print(num_sentences)\n", 122 | "print(len(corpus))\n", 123 | "print(corpus[1])\n", 124 | "\n", 125 | "# Expected Output:\n", 126 | "# 1600000\n", 127 | "# 1600000\n", 128 | "# [\"is upset that he can't update his Facebook by texting it... and might cry as a result School today also. Blah!\", 0]" 129 | ], 130 | "execution_count": 0, 131 | "outputs": [ 132 | { 133 | "output_type": "stream", 134 | "text": [ 135 | "1600000\n", 136 | "1600000\n", 137 | "[\"is upset that he can't update his Facebook by texting it... and might cry as a result School today also. Blah!\", 0]\n" 138 | ], 139 | "name": "stdout" 140 | } 141 | ] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "metadata": { 146 | "id": "ohOGz24lsNAD", 147 | "colab_type": "code", 148 | "colab": {} 149 | }, 150 | "source": [ 151 | "sentences=[]\n", 152 | "labels=[]\n", 153 | "random.shuffle(corpus)\n", 154 | "for x in range(training_size):\n", 155 | " sentences.append(corpus[x][0])\n", 156 | " labels.append(corpus[x][1])\n", 157 | "\n", 158 | "\n", 159 | "tokenizer = Tokenizer()\n", 160 | "tokenizer.fit_on_texts(sentences)\n", 161 | "\n", 162 | "word_index = tokenizer.word_index\n", 163 | "vocab_size=len(word_index)\n", 164 | "\n", 165 | "sequences = tokenizer.texts_to_sequences(sentences)\n", 166 | "padded = pad_sequences(sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n", 167 | "\n", 168 | "split = int(test_portion * training_size)\n", 169 | "\n", 170 | "test_sequences = padded[0:split]\n", 171 | "training_sequences = padded[split:training_size]\n", 172 | "test_labels = labels[0:split]\n", 173 | "training_labels = labels[split:training_size]" 174 | ], 175 | "execution_count": 0, 176 | "outputs": [] 177 | }, 178 | { 179 | "cell_type": "code", 180 | "metadata": { 181 | "id": "gIrtRem1En3N", 182 | "colab_type": "code", 183 | "outputId": "4ad8401c-8dba-420d-8aee-38dac0b0839a", 184 | "colab": { 185 | "base_uri": "https://localhost:8080/", 186 | "height": 51 187 | } 188 | }, 189 | "source": [ 190 | "print(vocab_size)\n", 191 | "print(word_index['i'])\n", 192 | "# Expected Output\n", 193 | "# 138858\n", 194 | "# 1" 195 | ], 196 | "execution_count": 0, 197 | "outputs": [ 198 | { 199 | "output_type": "stream", 200 | "text": [ 201 | "138858\n", 202 | "1\n" 203 | ], 204 | "name": "stdout" 205 | } 206 | ] 207 | }, 208 | { 209 | "cell_type": "code", 210 | "metadata": { 211 | "id": "C1zdgJkusRh0", 212 | "colab_type": "code", 213 | "outputId": "b6edd322-8191-45e7-cb12-08921685a72f", 214 | "colab": { 215 | "base_uri": "https://localhost:8080/", 216 | "height": 204 217 | } 218 | }, 219 | "source": [ 220 | "# Note this is the 100 dimension version of GloVe from Stanford\n", 221 | "# I unzipped and hosted it on my site to make this notebook easier\n", 222 | "!wget --no-check-certificate \\\n", 223 | " https://storage.googleapis.com/laurencemoroney-blog.appspot.com/glove.6B.100d.txt \\\n", 224 | " -O /tmp/glove.6B.100d.txt\n", 225 | "embeddings_index = {};\n", 226 | "with open('/tmp/glove.6B.100d.txt') as f:\n", 227 | " for line in f:\n", 228 | " values = line.split();\n", 229 | " word = values[0];\n", 230 | " coefs = np.asarray(values[1:], dtype='float32');\n", 231 | " embeddings_index[word] = coefs;\n", 232 | "\n", 233 | "embeddings_matrix = np.zeros((vocab_size+1, embedding_dim));\n", 234 | "for word, i in word_index.items():\n", 235 | " embedding_vector = embeddings_index.get(word);\n", 236 | " if embedding_vector is not None:\n", 237 | " embeddings_matrix[i] = embedding_vector;" 238 | ], 239 | "execution_count": 0, 240 | "outputs": [ 241 | { 242 | "output_type": "stream", 243 | "text": [ 244 | "--2019-06-07 17:55:30-- https://storage.googleapis.com/laurencemoroney-blog.appspot.com/glove.6B.100d.txt\n", 245 | "Resolving storage.googleapis.com (storage.googleapis.com)... 64.233.183.128, 2607:f8b0:4001:c12::80\n", 246 | "Connecting to storage.googleapis.com (storage.googleapis.com)|64.233.183.128|:443... connected.\n", 247 | "HTTP request sent, awaiting response... 200 OK\n", 248 | "Length: 347116733 (331M) [text/plain]\n", 249 | "Saving to: ‘/tmp/glove.6B.100d.txt’\n", 250 | "\n", 251 | "/tmp/glove.6B.100d. 100%[===================>] 331.04M 160MB/s in 2.1s \n", 252 | "\n", 253 | "2019-06-07 17:55:33 (160 MB/s) - ‘/tmp/glove.6B.100d.txt’ saved [347116733/347116733]\n", 254 | "\n" 255 | ], 256 | "name": "stdout" 257 | } 258 | ] 259 | }, 260 | { 261 | "cell_type": "code", 262 | "metadata": { 263 | "id": "71NLk_lpFLNt", 264 | "colab_type": "code", 265 | "outputId": "97cb88db-754f-4375-fdc3-876cd6b4fdce", 266 | "colab": { 267 | "base_uri": "https://localhost:8080/", 268 | "height": 34 269 | } 270 | }, 271 | "source": [ 272 | "print(len(embeddings_matrix))\n", 273 | "# Expected Output\n", 274 | "# 138859" 275 | ], 276 | "execution_count": 0, 277 | "outputs": [ 278 | { 279 | "output_type": "stream", 280 | "text": [ 281 | "138859\n" 282 | ], 283 | "name": "stdout" 284 | } 285 | ] 286 | }, 287 | { 288 | "cell_type": "code", 289 | "metadata": { 290 | "colab_type": "code", 291 | "id": "iKKvbuEBOGFz", 292 | "colab": {} 293 | }, 294 | "source": [ 295 | "model = tf.keras.Sequential([\n", 296 | " tf.keras.layers.Embedding(vocab_size+1, embedding_dim, input_length=max_length, weights=[embeddings_matrix], trainable=False),\n", 297 | " tf.keras.layers.Dropout(0.2),\n", 298 | " tf.keras.layers.Conv1D(64, 5, activation='relu'),\n", 299 | " tf.keras.layers.MaxPooling1D(pool_size=4),\n", 300 | " tf.keras.layers.LSTM(64),\n", 301 | " tf.keras.layers.Dense(1, activation='sigmoid')\n", 302 | "])\n", 303 | "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n", 304 | "model.summary()\n", 305 | "\n", 306 | "num_epochs = 50\n", 307 | "history = model.fit(training_sequences, training_labels, epochs=num_epochs, validation_data=(test_sequences, test_labels), verbose=2)\n", 308 | "\n", 309 | "print(\"Training Complete\")\n" 310 | ], 311 | "execution_count": 0, 312 | "outputs": [] 313 | }, 314 | { 315 | "cell_type": "code", 316 | "metadata": { 317 | "id": "qxju4ItJKO8F", 318 | "colab_type": "code", 319 | "colab": {} 320 | }, 321 | "source": [ 322 | "import matplotlib.image as mpimg\n", 323 | "import matplotlib.pyplot as plt\n", 324 | "\n", 325 | "#-----------------------------------------------------------\n", 326 | "# Retrieve a list of list results on training and test data\n", 327 | "# sets for each training epoch\n", 328 | "#-----------------------------------------------------------\n", 329 | "acc=history.history['acc']\n", 330 | "val_acc=history.history['val_acc']\n", 331 | "loss=history.history['loss']\n", 332 | "val_loss=history.history['val_loss']\n", 333 | "\n", 334 | "epochs=range(len(acc)) # Get number of epochs\n", 335 | "\n", 336 | "#------------------------------------------------\n", 337 | "# Plot training and validation accuracy per epoch\n", 338 | "#------------------------------------------------\n", 339 | "plt.plot(epochs, acc, 'r')\n", 340 | "plt.plot(epochs, val_acc, 'b')\n", 341 | "plt.title('Training and validation accuracy')\n", 342 | "plt.xlabel(\"Epochs\")\n", 343 | "plt.ylabel(\"Accuracy\")\n", 344 | "plt.legend([\"Accuracy\", \"Validation Accuracy\"])\n", 345 | "\n", 346 | "plt.figure()\n", 347 | "\n", 348 | "#------------------------------------------------\n", 349 | "# Plot training and validation loss per epoch\n", 350 | "#------------------------------------------------\n", 351 | "plt.plot(epochs, loss, 'r')\n", 352 | "plt.plot(epochs, val_loss, 'b')\n", 353 | "plt.title('Training and validation loss')\n", 354 | "plt.xlabel(\"Epochs\")\n", 355 | "plt.ylabel(\"Loss\")\n", 356 | "plt.legend([\"Loss\", \"Validation Loss\"])\n", 357 | "\n", 358 | "plt.figure()\n", 359 | "\n", 360 | "\n", 361 | "# Expected Output\n", 362 | "# A chart where the validation loss does not increase sharply!" 363 | ], 364 | "execution_count": 0, 365 | "outputs": [] 366 | } 367 | ] 368 | } -------------------------------------------------------------------------------- /3 - Natural Langu age Processing in TensorFlow/Week 3/Week 3 Quiz.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/3 - Natural Langu age Processing in TensorFlow/Week 3/Week 3 Quiz.pdf -------------------------------------------------------------------------------- /3 - Natural Langu age Processing in TensorFlow/Week 3/Week 3 Quiz.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/3 - Natural Langu age Processing in TensorFlow/Week 3/Week 3 Quiz.png -------------------------------------------------------------------------------- /3 - Natural Langu age Processing in TensorFlow/Week 4/Course 3 Week 4.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "NLP_Week4_Exercise_Shakespeare_Answer.ipynb", 7 | "provenance": [] 8 | }, 9 | "kernelspec": { 10 | "name": "python3", 11 | "display_name": "Python 3" 12 | }, 13 | "accelerator": "GPU" 14 | }, 15 | "cells": [ 16 | { 17 | "cell_type": "code", 18 | "metadata": { 19 | "id": "BOwsuGQQY9OL", 20 | "colab_type": "code", 21 | "colab": {} 22 | }, 23 | "source": [ 24 | "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", 25 | "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout, Bidirectional\n", 26 | "from tensorflow.keras.preprocessing.text import Tokenizer\n", 27 | "from tensorflow.keras.models import Sequential\n", 28 | "from tensorflow.keras.optimizers import Adam\n", 29 | "from tensorflow.keras import regularizers\n", 30 | "import tensorflow.keras.utils as ku \n", 31 | "import numpy as np " 32 | ], 33 | "execution_count": 0, 34 | "outputs": [] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "metadata": { 39 | "colab_type": "code", 40 | "id": "PRnDnCW-Z7qv", 41 | "colab": {} 42 | }, 43 | "source": [ 44 | "tokenizer = Tokenizer()\n", 45 | "!wget --no-check-certificate \\\n", 46 | " https://storage.googleapis.com/laurencemoroney-blog.appspot.com/sonnets.txt \\\n", 47 | " -O /tmp/sonnets.txt\n", 48 | "data = open('/tmp/sonnets.txt').read()\n", 49 | "\n", 50 | "corpus = data.lower().split(\"\\n\")\n", 51 | "\n", 52 | "\n", 53 | "tokenizer.fit_on_texts(corpus)\n", 54 | "total_words = len(tokenizer.word_index) + 1\n", 55 | "\n", 56 | "# create input sequences using list of tokens\n", 57 | "input_sequences = []\n", 58 | "for line in corpus:\n", 59 | "\ttoken_list = tokenizer.texts_to_sequences([line])[0]\n", 60 | "\tfor i in range(1, len(token_list)):\n", 61 | "\t\tn_gram_sequence = token_list[:i+1]\n", 62 | "\t\tinput_sequences.append(n_gram_sequence)\n", 63 | "\n", 64 | "\n", 65 | "# pad sequences \n", 66 | "max_sequence_len = max([len(x) for x in input_sequences])\n", 67 | "input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))\n", 68 | "\n", 69 | "# create predictors and label\n", 70 | "predictors, label = input_sequences[:,:-1],input_sequences[:,-1]\n", 71 | "\n", 72 | "label = ku.to_categorical(label, num_classes=total_words)" 73 | ], 74 | "execution_count": 0, 75 | "outputs": [] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "metadata": { 80 | "id": "w9vH8Y59ajYL", 81 | "colab_type": "code", 82 | "colab": {} 83 | }, 84 | "source": [ 85 | "model = Sequential()\n", 86 | "model.add(Embedding(total_words, 100, input_length=max_sequence_len-1))\n", 87 | "model.add(Bidirectional(LSTM(150, return_sequences = True)))\n", 88 | "model.add(Dropout(0.2))\n", 89 | "model.add(LSTM(100))\n", 90 | "model.add(Dense(total_words/2, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n", 91 | "model.add(Dense(total_words, activation='softmax'))\n", 92 | "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", 93 | "print(model.summary())\n" 94 | ], 95 | "execution_count": 0, 96 | "outputs": [] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "metadata": { 101 | "id": "AIg2f1HBxqof", 102 | "colab_type": "code", 103 | "colab": {} 104 | }, 105 | "source": [ 106 | " history = model.fit(predictors, label, epochs=100, verbose=1)" 107 | ], 108 | "execution_count": 0, 109 | "outputs": [] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "metadata": { 114 | "id": "1fXTEO3GJ282", 115 | "colab_type": "code", 116 | "colab": {} 117 | }, 118 | "source": [ 119 | "import matplotlib.pyplot as plt\n", 120 | "acc = history.history['acc']\n", 121 | "loss = history.history['loss']\n", 122 | "\n", 123 | "epochs = range(len(acc))\n", 124 | "\n", 125 | "plt.plot(epochs, acc, 'b', label='Training accuracy')\n", 126 | "plt.title('Training accuracy')\n", 127 | "\n", 128 | "plt.figure()\n", 129 | "\n", 130 | "plt.plot(epochs, loss, 'b', label='Training Loss')\n", 131 | "plt.title('Training loss')\n", 132 | "plt.legend()\n", 133 | "\n", 134 | "plt.show()" 135 | ], 136 | "execution_count": 0, 137 | "outputs": [] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "metadata": { 142 | "id": "6Vc6PHgxa6Hm", 143 | "colab_type": "code", 144 | "colab": {} 145 | }, 146 | "source": [ 147 | "seed_text = \"Help me Obi Wan Kenobi, you're my only hope\"\n", 148 | "next_words = 100\n", 149 | " \n", 150 | "for _ in range(next_words):\n", 151 | "\ttoken_list = tokenizer.texts_to_sequences([seed_text])[0]\n", 152 | "\ttoken_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')\n", 153 | "\tpredicted = model.predict_classes(token_list, verbose=0)\n", 154 | "\toutput_word = \"\"\n", 155 | "\tfor word, index in tokenizer.word_index.items():\n", 156 | "\t\tif index == predicted:\n", 157 | "\t\t\toutput_word = word\n", 158 | "\t\t\tbreak\n", 159 | "\tseed_text += \" \" + output_word\n", 160 | "print(seed_text)" 161 | ], 162 | "execution_count": 0, 163 | "outputs": [] 164 | } 165 | ] 166 | } -------------------------------------------------------------------------------- /3 - Natural Langu age Processing in TensorFlow/Week 4/Week 4 Quiz.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/3 - Natural Langu age Processing in TensorFlow/Week 4/Week 4 Quiz.pdf -------------------------------------------------------------------------------- /3 - Natural Langu age Processing in TensorFlow/Week 4/Week 4 Quiz.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/3 - Natural Langu age Processing in TensorFlow/Week 4/Week 4 Quiz.png -------------------------------------------------------------------------------- /4 - Sequences, Time Series and Prediction/Sequ ences, Time Series and Prediction Certificate.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/4 - Sequences, Time Series and Prediction/Sequ ences, Time Series and Prediction Certificate.pdf -------------------------------------------------------------------------------- /4 - Sequences, Time Series and Prediction/Week 1/Course 4 Week 1.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Week 1 Exercise Answer.ipynb", 7 | "provenance": [], 8 | "collapsed_sections": [] 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "accelerator": "GPU" 15 | }, 16 | "cells": [ 17 | { 18 | "cell_type": "code", 19 | "metadata": { 20 | "id": "y7QztBIVR1tb", 21 | "colab_type": "code", 22 | "colab": {} 23 | }, 24 | "source": [ 25 | "!pip install tensorflow==2.0.0b1\n" 26 | ], 27 | "execution_count": 0, 28 | "outputs": [] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "metadata": { 33 | "id": "t9HrvPfrSlzS", 34 | "colab_type": "code", 35 | "colab": {} 36 | }, 37 | "source": [ 38 | "import tensorflow as tf\n", 39 | "print(tf.__version__)\n" 40 | ], 41 | "execution_count": 0, 42 | "outputs": [] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "metadata": { 47 | "id": "gqWabzlJ63nL", 48 | "colab_type": "code", 49 | "colab": {} 50 | }, 51 | "source": [ 52 | "import numpy as np\n", 53 | "import matplotlib.pyplot as plt\n", 54 | "import tensorflow as tf\n", 55 | "from tensorflow import keras\n", 56 | "\n", 57 | "def plot_series(time, series, format=\"-\", start=0, end=None):\n", 58 | " plt.plot(time[start:end], series[start:end], format)\n", 59 | " plt.xlabel(\"Time\")\n", 60 | " plt.ylabel(\"Value\")\n", 61 | " plt.grid(True)\n", 62 | "\n", 63 | "def trend(time, slope=0):\n", 64 | " return slope * time\n", 65 | "\n", 66 | "def seasonal_pattern(season_time):\n", 67 | " \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n", 68 | " return np.where(season_time < 0.1,\n", 69 | " np.cos(season_time * 7 * np.pi),\n", 70 | " 1 / np.exp(5 * season_time))\n", 71 | "\n", 72 | "def seasonality(time, period, amplitude=1, phase=0):\n", 73 | " \"\"\"Repeats the same pattern at each period\"\"\"\n", 74 | " season_time = ((time + phase) % period) / period\n", 75 | " return amplitude * seasonal_pattern(season_time)\n", 76 | "\n", 77 | "def noise(time, noise_level=1, seed=None):\n", 78 | " rnd = np.random.RandomState(seed)\n", 79 | " return rnd.randn(len(time)) * noise_level\n", 80 | "\n", 81 | "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n", 82 | "baseline = 10\n", 83 | "series = trend(time, 0.1) \n", 84 | "baseline = 10\n", 85 | "amplitude = 40\n", 86 | "slope = 0.01\n", 87 | "noise_level = 2\n", 88 | "\n", 89 | "# Create the series\n", 90 | "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n", 91 | "# Update with noise\n", 92 | "series += noise(time, noise_level, seed=42)\n", 93 | "\n", 94 | "plt.figure(figsize=(10, 6))\n", 95 | "plot_series(time, series)\n", 96 | "plt.show()" 97 | ], 98 | "execution_count": 0, 99 | "outputs": [] 100 | }, 101 | { 102 | "cell_type": "markdown", 103 | "metadata": { 104 | "id": "UfdyqJJ1VZVu", 105 | "colab_type": "text" 106 | }, 107 | "source": [ 108 | "Now that we have the time series, let's split it so we can start forecasting" 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "metadata": { 114 | "id": "_w0eKap5uFNP", 115 | "colab_type": "code", 116 | "colab": {} 117 | }, 118 | "source": [ 119 | "split_time = 1100\n", 120 | "time_train = time[:split_time]\n", 121 | "x_train = series[:split_time]\n", 122 | "time_valid = time[split_time:]\n", 123 | "x_valid = series[split_time:]\n", 124 | "plt.figure(figsize=(10, 6))\n", 125 | "plot_series(time_train, x_train)\n", 126 | "plt.show()\n", 127 | "\n", 128 | "plt.figure(figsize=(10, 6))\n", 129 | "plot_series(time_valid, x_valid)\n", 130 | "plt.show()" 131 | ], 132 | "execution_count": 0, 133 | "outputs": [] 134 | }, 135 | { 136 | "cell_type": "markdown", 137 | "metadata": { 138 | "id": "bjD8ncEZbjEW", 139 | "colab_type": "text" 140 | }, 141 | "source": [ 142 | "# Naive Forecast" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "metadata": { 148 | "id": "Pj_-uCeYxcAb", 149 | "colab_type": "code", 150 | "colab": {} 151 | }, 152 | "source": [ 153 | "naive_forecast = series[split_time - 1:-1]" 154 | ], 155 | "execution_count": 0, 156 | "outputs": [] 157 | }, 158 | { 159 | "cell_type": "code", 160 | "metadata": { 161 | "id": "JtxwHj9Ig0jT", 162 | "colab_type": "code", 163 | "colab": {} 164 | }, 165 | "source": [ 166 | "plt.figure(figsize=(10, 6))\n", 167 | "plot_series(time_valid, x_valid)\n", 168 | "plot_series(time_valid, naive_forecast)" 169 | ], 170 | "execution_count": 0, 171 | "outputs": [] 172 | }, 173 | { 174 | "cell_type": "markdown", 175 | "metadata": { 176 | "id": "fw1SP5WeuixH", 177 | "colab_type": "text" 178 | }, 179 | "source": [ 180 | "Let's zoom in on the start of the validation period:" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "metadata": { 186 | "id": "D0MKg7FNug9V", 187 | "colab_type": "code", 188 | "colab": {} 189 | }, 190 | "source": [ 191 | "plt.figure(figsize=(10, 6))\n", 192 | "plot_series(time_valid, x_valid, start=0, end=150)\n", 193 | "plot_series(time_valid, naive_forecast, start=1, end=151)" 194 | ], 195 | "execution_count": 0, 196 | "outputs": [] 197 | }, 198 | { 199 | "cell_type": "markdown", 200 | "metadata": { 201 | "id": "35gIlQLfu0TT", 202 | "colab_type": "text" 203 | }, 204 | "source": [ 205 | "You can see that the naive forecast lags 1 step behind the time series." 206 | ] 207 | }, 208 | { 209 | "cell_type": "markdown", 210 | "metadata": { 211 | "id": "Uh_7244Gsxfx", 212 | "colab_type": "text" 213 | }, 214 | "source": [ 215 | "Now let's compute the mean squared error and the mean absolute error between the forecasts and the predictions in the validation period:" 216 | ] 217 | }, 218 | { 219 | "cell_type": "code", 220 | "metadata": { 221 | "id": "byNnC7IbsnMZ", 222 | "colab_type": "code", 223 | "colab": {} 224 | }, 225 | "source": [ 226 | "print(keras.metrics.mean_squared_error(x_valid, naive_forecast).numpy())\n", 227 | "print(keras.metrics.mean_absolute_error(x_valid, naive_forecast).numpy())" 228 | ], 229 | "execution_count": 0, 230 | "outputs": [] 231 | }, 232 | { 233 | "cell_type": "markdown", 234 | "metadata": { 235 | "id": "WGPBC9QttI1u", 236 | "colab_type": "text" 237 | }, 238 | "source": [ 239 | "That's our baseline, now let's try a moving average:" 240 | ] 241 | }, 242 | { 243 | "cell_type": "code", 244 | "metadata": { 245 | "id": "YGz5UsUdf2tV", 246 | "colab_type": "code", 247 | "colab": {} 248 | }, 249 | "source": [ 250 | "def moving_average_forecast(series, window_size):\n", 251 | " \"\"\"Forecasts the mean of the last few values.\n", 252 | " If window_size=1, then this is equivalent to naive forecast\"\"\"\n", 253 | " forecast = []\n", 254 | " for time in range(len(series) - window_size):\n", 255 | " forecast.append(series[time:time + window_size].mean())\n", 256 | " return np.array(forecast)" 257 | ], 258 | "execution_count": 0, 259 | "outputs": [] 260 | }, 261 | { 262 | "cell_type": "code", 263 | "metadata": { 264 | "id": "HHFhGXQji7_r", 265 | "colab_type": "code", 266 | "colab": {} 267 | }, 268 | "source": [ 269 | "moving_avg = moving_average_forecast(series, 30)[split_time - 30:]\n", 270 | "\n", 271 | "plt.figure(figsize=(10, 6))\n", 272 | "plot_series(time_valid, x_valid)\n", 273 | "plot_series(time_valid, moving_avg)" 274 | ], 275 | "execution_count": 0, 276 | "outputs": [] 277 | }, 278 | { 279 | "cell_type": "code", 280 | "metadata": { 281 | "id": "wG7pTAd7z0e8", 282 | "colab_type": "code", 283 | "colab": {} 284 | }, 285 | "source": [ 286 | "print(keras.metrics.mean_squared_error(x_valid, moving_avg).numpy())\n", 287 | "print(keras.metrics.mean_absolute_error(x_valid, moving_avg).numpy())" 288 | ], 289 | "execution_count": 0, 290 | "outputs": [] 291 | }, 292 | { 293 | "cell_type": "markdown", 294 | "metadata": { 295 | "id": "JMYPnJqwz8nS", 296 | "colab_type": "text" 297 | }, 298 | "source": [ 299 | "That's worse than naive forecast! The moving average does not anticipate trend or seasonality, so let's try to remove them by using differencing. Since the seasonality period is 365 days, we will subtract the value at time *t* – 365 from the value at time *t*." 300 | ] 301 | }, 302 | { 303 | "cell_type": "code", 304 | "metadata": { 305 | "id": "5pqySF7-rJR4", 306 | "colab_type": "code", 307 | "colab": {} 308 | }, 309 | "source": [ 310 | "diff_series = (series[365:] - series[:-365])\n", 311 | "diff_time = time[365:]\n", 312 | "\n", 313 | "plt.figure(figsize=(10, 6))\n", 314 | "plot_series(diff_time, diff_series)\n", 315 | "plt.show()" 316 | ], 317 | "execution_count": 0, 318 | "outputs": [] 319 | }, 320 | { 321 | "cell_type": "markdown", 322 | "metadata": { 323 | "id": "xPlPlS7DskWg", 324 | "colab_type": "text" 325 | }, 326 | "source": [ 327 | "Great, the trend and seasonality seem to be gone, so now we can use the moving average:" 328 | ] 329 | }, 330 | { 331 | "cell_type": "code", 332 | "metadata": { 333 | "id": "QmZpz7arsjbb", 334 | "colab_type": "code", 335 | "colab": {} 336 | }, 337 | "source": [ 338 | "diff_moving_avg = moving_average_forecast(diff_series, 50)[split_time - 365 - 50:]\n", 339 | "\n", 340 | "plt.figure(figsize=(10, 6))\n", 341 | "plot_series(time_valid, diff_series[split_time - 365:])\n", 342 | "plot_series(time_valid, diff_moving_avg)\n", 343 | "plt.show()" 344 | ], 345 | "execution_count": 0, 346 | "outputs": [] 347 | }, 348 | { 349 | "cell_type": "markdown", 350 | "metadata": { 351 | "id": "Gno9S2lyecnc", 352 | "colab_type": "text" 353 | }, 354 | "source": [ 355 | "Now let's bring back the trend and seasonality by adding the past values from t – 365:" 356 | ] 357 | }, 358 | { 359 | "cell_type": "code", 360 | "metadata": { 361 | "id": "Dv6RWFq7TFGB", 362 | "colab_type": "code", 363 | "colab": {} 364 | }, 365 | "source": [ 366 | "diff_moving_avg_plus_past = series[split_time - 365:-365] + diff_moving_avg\n", 367 | "\n", 368 | "plt.figure(figsize=(10, 6))\n", 369 | "plot_series(time_valid, x_valid)\n", 370 | "plot_series(time_valid, diff_moving_avg_plus_past)\n", 371 | "plt.show()" 372 | ], 373 | "execution_count": 0, 374 | "outputs": [] 375 | }, 376 | { 377 | "cell_type": "code", 378 | "metadata": { 379 | "id": "59jmBrwcTFCx", 380 | "colab_type": "code", 381 | "colab": {} 382 | }, 383 | "source": [ 384 | "\n", 385 | "print(keras.metrics.mean_squared_error(x_valid, diff_moving_avg_plus_past).numpy())\n", 386 | "print(keras.metrics.mean_absolute_error(x_valid, diff_moving_avg_plus_past).numpy())" 387 | ], 388 | "execution_count": 0, 389 | "outputs": [] 390 | }, 391 | { 392 | "cell_type": "markdown", 393 | "metadata": { 394 | "id": "vx9Et1Hkeusl", 395 | "colab_type": "text" 396 | }, 397 | "source": [ 398 | "Better than naive forecast, good. However the forecasts look a bit too random, because we're just adding past values, which were noisy. Let's use a moving averaging on past values to remove some of the noise:" 399 | ] 400 | }, 401 | { 402 | "cell_type": "code", 403 | "metadata": { 404 | "id": "K81dtROoTE_r", 405 | "colab_type": "code", 406 | "colab": {} 407 | }, 408 | "source": [ 409 | "diff_moving_avg_plus_smooth_past = moving_average_forecast(series[split_time - 370:-360], 10) + diff_moving_avg\n", 410 | "\n", 411 | "plt.figure(figsize=(10, 6))\n", 412 | "plot_series(time_valid, x_valid)\n", 413 | "plot_series(time_valid, diff_moving_avg_plus_smooth_past)\n", 414 | "plt.show()" 415 | ], 416 | "execution_count": 0, 417 | "outputs": [] 418 | }, 419 | { 420 | "cell_type": "code", 421 | "metadata": { 422 | "id": "iN2MsBxWTE3m", 423 | "colab_type": "code", 424 | "colab": {} 425 | }, 426 | "source": [ 427 | "print(keras.metrics.mean_squared_error(x_valid, diff_moving_avg_plus_smooth_past).numpy())\n", 428 | "print(keras.metrics.mean_absolute_error(x_valid, diff_moving_avg_plus_smooth_past).numpy())" 429 | ], 430 | "execution_count": 0, 431 | "outputs": [] 432 | } 433 | ] 434 | } -------------------------------------------------------------------------------- /4 - Sequences, Time Series and Prediction/Week 1/Week 1 Quiz.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/4 - Sequences, Time Series and Prediction/Week 1/Week 1 Quiz.pdf -------------------------------------------------------------------------------- /4 - Sequences, Time Series and Prediction/Week 1/Week 1 Quiz.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/4 - Sequences, Time Series and Prediction/Week 1/Week 1 Quiz.png -------------------------------------------------------------------------------- /4 - Sequences, Time Series and Prediction/Week 2/Course 4 Week 2.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "S+P Week 2 Exercise Answer.ipynb", 7 | "provenance": [] 8 | }, 9 | "kernelspec": { 10 | "name": "python3", 11 | "display_name": "Python 3" 12 | }, 13 | "accelerator": "GPU" 14 | }, 15 | "cells": [ 16 | { 17 | "cell_type": "code", 18 | "metadata": { 19 | "id": "D1J15Vh_1Jih", 20 | "colab_type": "code", 21 | "cellView": "both", 22 | "colab": {} 23 | }, 24 | "source": [ 25 | "!pip install tf-nightly-2.0-preview\n" 26 | ], 27 | "execution_count": 0, 28 | "outputs": [] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "metadata": { 33 | "id": "BOjujz601HcS", 34 | "colab_type": "code", 35 | "colab": {} 36 | }, 37 | "source": [ 38 | "import tensorflow as tf\n", 39 | "import numpy as np\n", 40 | "import matplotlib.pyplot as plt\n", 41 | "print(tf.__version__)" 42 | ], 43 | "execution_count": 0, 44 | "outputs": [] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "metadata": { 49 | "colab_type": "code", 50 | "id": "Zswl7jRtGzkk", 51 | "colab": {} 52 | }, 53 | "source": [ 54 | "def plot_series(time, series, format=\"-\", start=0, end=None):\n", 55 | " plt.plot(time[start:end], series[start:end], format)\n", 56 | " plt.xlabel(\"Time\")\n", 57 | " plt.ylabel(\"Value\")\n", 58 | " plt.grid(False)\n", 59 | "\n", 60 | "def trend(time, slope=0):\n", 61 | " return slope * time\n", 62 | "\n", 63 | "def seasonal_pattern(season_time):\n", 64 | " \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n", 65 | " return np.where(season_time < 0.1,\n", 66 | " np.cos(season_time * 6 * np.pi),\n", 67 | " 2 / np.exp(9 * season_time))\n", 68 | "\n", 69 | "def seasonality(time, period, amplitude=1, phase=0):\n", 70 | " \"\"\"Repeats the same pattern at each period\"\"\"\n", 71 | " season_time = ((time + phase) % period) / period\n", 72 | " return amplitude * seasonal_pattern(season_time)\n", 73 | "\n", 74 | "def noise(time, noise_level=1, seed=None):\n", 75 | " rnd = np.random.RandomState(seed)\n", 76 | " return rnd.randn(len(time)) * noise_level\n", 77 | "\n", 78 | "time = np.arange(10 * 365 + 1, dtype=\"float32\")\n", 79 | "baseline = 10\n", 80 | "series = trend(time, 0.1) \n", 81 | "baseline = 10\n", 82 | "amplitude = 40\n", 83 | "slope = 0.005\n", 84 | "noise_level = 3\n", 85 | "\n", 86 | "# Create the series\n", 87 | "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n", 88 | "# Update with noise\n", 89 | "series += noise(time, noise_level, seed=51)\n", 90 | "\n", 91 | "split_time = 3000\n", 92 | "time_train = time[:split_time]\n", 93 | "x_train = series[:split_time]\n", 94 | "time_valid = time[split_time:]\n", 95 | "x_valid = series[split_time:]\n", 96 | "\n", 97 | "window_size = 20\n", 98 | "batch_size = 32\n", 99 | "shuffle_buffer_size = 1000\n", 100 | "\n", 101 | "plot_series(time, series)" 102 | ], 103 | "execution_count": 0, 104 | "outputs": [] 105 | }, 106 | { 107 | "cell_type": "code", 108 | "metadata": { 109 | "id": "4sTTIOCbyShY", 110 | "colab_type": "code", 111 | "colab": {} 112 | }, 113 | "source": [ 114 | "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n", 115 | " dataset = tf.data.Dataset.from_tensor_slices(series)\n", 116 | " dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n", 117 | " dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n", 118 | " dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n", 119 | " dataset = dataset.batch(batch_size).prefetch(1)\n", 120 | " return dataset" 121 | ], 122 | "execution_count": 0, 123 | "outputs": [] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "metadata": { 128 | "id": "TW-vT7eLYAdb", 129 | "colab_type": "code", 130 | "colab": {} 131 | }, 132 | "source": [ 133 | "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n", 134 | "\n", 135 | "\n", 136 | "model = tf.keras.models.Sequential([\n", 137 | " tf.keras.layers.Dense(100, input_shape=[window_size], activation=\"relu\"), \n", 138 | " tf.keras.layers.Dense(10, activation=\"relu\"), \n", 139 | " tf.keras.layers.Dense(1)\n", 140 | "])\n", 141 | "\n", 142 | "model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(lr=1e-6, momentum=0.9))\n", 143 | "model.fit(dataset,epochs=100,verbose=0)\n", 144 | "\n" 145 | ], 146 | "execution_count": 0, 147 | "outputs": [] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "metadata": { 152 | "id": "efhco2rYyIFF", 153 | "colab_type": "code", 154 | "colab": {} 155 | }, 156 | "source": [ 157 | "forecast = []\n", 158 | "for time in range(len(series) - window_size):\n", 159 | " forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n", 160 | "\n", 161 | "forecast = forecast[split_time-window_size:]\n", 162 | "results = np.array(forecast)[:, 0, 0]\n", 163 | "\n", 164 | "\n", 165 | "plt.figure(figsize=(10, 6))\n", 166 | "\n", 167 | "plot_series(time_valid, x_valid)\n", 168 | "plot_series(time_valid, results)" 169 | ], 170 | "execution_count": 0, 171 | "outputs": [] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "metadata": { 176 | "id": "-kT6j186YO6K", 177 | "colab_type": "code", 178 | "colab": {} 179 | }, 180 | "source": [ 181 | "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()" 182 | ], 183 | "execution_count": 0, 184 | "outputs": [] 185 | } 186 | ] 187 | } -------------------------------------------------------------------------------- /4 - Sequences, Time Series and Prediction/Week 2/Week 2 Quiz.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/4 - Sequences, Time Series and Prediction/Week 2/Week 2 Quiz.pdf -------------------------------------------------------------------------------- /4 - Sequences, Time Series and Prediction/Week 2/Week 2 Quiz.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/4 - Sequences, Time Series and Prediction/Week 2/Week 2 Quiz.png -------------------------------------------------------------------------------- /4 - Sequences, Time Series and Prediction/Week 3/Course 4 Week 3.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "S+P Week 3 Exercise Answer.ipynb", 7 | "provenance": [], 8 | "collapsed_sections": [] 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "accelerator": "GPU" 15 | }, 16 | "cells": [ 17 | { 18 | "cell_type": "code", 19 | "metadata": { 20 | "id": "D1J15Vh_1Jih", 21 | "colab_type": "code", 22 | "colab": {} 23 | }, 24 | "source": [ 25 | "!pip install tf-nightly-2.0-preview\n" 26 | ], 27 | "execution_count": 0, 28 | "outputs": [] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "metadata": { 33 | "id": "BOjujz601HcS", 34 | "colab_type": "code", 35 | "colab": {} 36 | }, 37 | "source": [ 38 | "import tensorflow as tf\n", 39 | "import numpy as np\n", 40 | "import matplotlib.pyplot as plt\n", 41 | "print(tf.__version__)" 42 | ], 43 | "execution_count": 0, 44 | "outputs": [] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "metadata": { 49 | "colab_type": "code", 50 | "id": "Zswl7jRtGzkk", 51 | "colab": {} 52 | }, 53 | "source": [ 54 | "def plot_series(time, series, format=\"-\", start=0, end=None):\n", 55 | " plt.plot(time[start:end], series[start:end], format)\n", 56 | " plt.xlabel(\"Time\")\n", 57 | " plt.ylabel(\"Value\")\n", 58 | " plt.grid(False)\n", 59 | "\n", 60 | "def trend(time, slope=0):\n", 61 | " return slope * time\n", 62 | "\n", 63 | "def seasonal_pattern(season_time):\n", 64 | " \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n", 65 | " return np.where(season_time < 0.1,\n", 66 | " np.cos(season_time * 6 * np.pi),\n", 67 | " 2 / np.exp(9 * season_time))\n", 68 | "\n", 69 | "def seasonality(time, period, amplitude=1, phase=0):\n", 70 | " \"\"\"Repeats the same pattern at each period\"\"\"\n", 71 | " season_time = ((time + phase) % period) / period\n", 72 | " return amplitude * seasonal_pattern(season_time)\n", 73 | "\n", 74 | "def noise(time, noise_level=1, seed=None):\n", 75 | " rnd = np.random.RandomState(seed)\n", 76 | " return rnd.randn(len(time)) * noise_level\n", 77 | "\n", 78 | "time = np.arange(10 * 365 + 1, dtype=\"float32\")\n", 79 | "baseline = 10\n", 80 | "series = trend(time, 0.1) \n", 81 | "baseline = 10\n", 82 | "amplitude = 40\n", 83 | "slope = 0.005\n", 84 | "noise_level = 3\n", 85 | "\n", 86 | "# Create the series\n", 87 | "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n", 88 | "# Update with noise\n", 89 | "series += noise(time, noise_level, seed=51)\n", 90 | "\n", 91 | "split_time = 3000\n", 92 | "time_train = time[:split_time]\n", 93 | "x_train = series[:split_time]\n", 94 | "time_valid = time[split_time:]\n", 95 | "x_valid = series[split_time:]\n", 96 | "\n", 97 | "window_size = 20\n", 98 | "batch_size = 32\n", 99 | "shuffle_buffer_size = 1000\n", 100 | "\n", 101 | "plot_series(time, series)" 102 | ], 103 | "execution_count": 0, 104 | "outputs": [] 105 | }, 106 | { 107 | "cell_type": "code", 108 | "metadata": { 109 | "id": "4sTTIOCbyShY", 110 | "colab_type": "code", 111 | "colab": {} 112 | }, 113 | "source": [ 114 | "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n", 115 | " dataset = tf.data.Dataset.from_tensor_slices(series)\n", 116 | " dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n", 117 | " dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n", 118 | " dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n", 119 | " dataset = dataset.batch(batch_size).prefetch(1)\n", 120 | " return dataset" 121 | ], 122 | "execution_count": 0, 123 | "outputs": [] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "metadata": { 128 | "id": "A1Hl39rklkLm", 129 | "colab_type": "code", 130 | "colab": {} 131 | }, 132 | "source": [ 133 | "tf.keras.backend.clear_session()\n", 134 | "tf.random.set_seed(51)\n", 135 | "np.random.seed(51)\n", 136 | "\n", 137 | "tf.keras.backend.clear_session()\n", 138 | "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n", 139 | "\n", 140 | "model = tf.keras.models.Sequential([\n", 141 | " tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1),\n", 142 | " input_shape=[None]),\n", 143 | " tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32, return_sequences=True)),\n", 144 | " tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n", 145 | " tf.keras.layers.Dense(1),\n", 146 | " tf.keras.layers.Lambda(lambda x: x * 10.0)\n", 147 | "])\n", 148 | "\n", 149 | "lr_schedule = tf.keras.callbacks.LearningRateScheduler(\n", 150 | " lambda epoch: 1e-8 * 10**(epoch / 20))\n", 151 | "optimizer = tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9)\n", 152 | "model.compile(loss=tf.keras.losses.Huber(),\n", 153 | " optimizer=optimizer,\n", 154 | " metrics=[\"mae\"])\n", 155 | "history = model.fit(dataset, epochs=100, callbacks=[lr_schedule])" 156 | ], 157 | "execution_count": 0, 158 | "outputs": [] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "metadata": { 163 | "id": "AkBsrsXMzoWR", 164 | "colab_type": "code", 165 | "colab": {} 166 | }, 167 | "source": [ 168 | "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n", 169 | "plt.axis([1e-8, 1e-4, 0, 30])" 170 | ], 171 | "execution_count": 0, 172 | "outputs": [] 173 | }, 174 | { 175 | "cell_type": "code", 176 | "metadata": { 177 | "id": "4uh-97bpLZCA", 178 | "colab_type": "code", 179 | "colab": {} 180 | }, 181 | "source": [ 182 | "tf.keras.backend.clear_session()\n", 183 | "tf.random.set_seed(51)\n", 184 | "np.random.seed(51)\n", 185 | "\n", 186 | "tf.keras.backend.clear_session()\n", 187 | "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n", 188 | "\n", 189 | "model = tf.keras.models.Sequential([\n", 190 | " tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1),\n", 191 | " input_shape=[None]),\n", 192 | " tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32, return_sequences=True)),\n", 193 | " tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n", 194 | " tf.keras.layers.Dense(1),\n", 195 | " tf.keras.layers.Lambda(lambda x: x * 100.0)\n", 196 | "])\n", 197 | "\n", 198 | "\n", 199 | "model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(lr=1e-5, momentum=0.9),metrics=[\"mae\"])\n", 200 | "history = model.fit(dataset,epochs=500,verbose=1)" 201 | ], 202 | "execution_count": 0, 203 | "outputs": [] 204 | }, 205 | { 206 | "cell_type": "code", 207 | "metadata": { 208 | "id": "icGDaND7z0ne", 209 | "colab_type": "code", 210 | "colab": {} 211 | }, 212 | "source": [ 213 | "forecast = []\n", 214 | "results = []\n", 215 | "for time in range(len(series) - window_size):\n", 216 | " forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n", 217 | "\n", 218 | "forecast = forecast[split_time-window_size:]\n", 219 | "results = np.array(forecast)[:, 0, 0]\n", 220 | "\n", 221 | "\n", 222 | "plt.figure(figsize=(10, 6))\n", 223 | "\n", 224 | "plot_series(time_valid, x_valid)\n", 225 | "plot_series(time_valid, results)" 226 | ], 227 | "execution_count": 0, 228 | "outputs": [] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "metadata": { 233 | "id": "KfPeqI7rz4LD", 234 | "colab_type": "code", 235 | "colab": {} 236 | }, 237 | "source": [ 238 | "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()" 239 | ], 240 | "execution_count": 0, 241 | "outputs": [] 242 | }, 243 | { 244 | "cell_type": "code", 245 | "metadata": { 246 | "id": "JUsdZB_tzDLe", 247 | "colab_type": "code", 248 | "colab": {} 249 | }, 250 | "source": [ 251 | "import matplotlib.image as mpimg\n", 252 | "import matplotlib.pyplot as plt\n", 253 | "\n", 254 | "#-----------------------------------------------------------\n", 255 | "# Retrieve a list of list results on training and test data\n", 256 | "# sets for each training epoch\n", 257 | "#-----------------------------------------------------------\n", 258 | "mae=history.history['mae']\n", 259 | "loss=history.history['loss']\n", 260 | "\n", 261 | "epochs=range(len(loss)) # Get number of epochs\n", 262 | "\n", 263 | "#------------------------------------------------\n", 264 | "# Plot MAE and Loss\n", 265 | "#------------------------------------------------\n", 266 | "plt.plot(epochs, mae, 'r')\n", 267 | "plt.plot(epochs, loss, 'b')\n", 268 | "plt.title('MAE and Loss')\n", 269 | "plt.xlabel(\"Epochs\")\n", 270 | "plt.ylabel(\"Accuracy\")\n", 271 | "plt.legend([\"MAE\", \"Loss\"])\n", 272 | "\n", 273 | "plt.figure()\n", 274 | "\n", 275 | "epochs_zoom = epochs[200:]\n", 276 | "mae_zoom = mae[200:]\n", 277 | "loss_zoom = loss[200:]\n", 278 | "\n", 279 | "#------------------------------------------------\n", 280 | "# Plot Zoomed MAE and Loss\n", 281 | "#------------------------------------------------\n", 282 | "plt.plot(epochs_zoom, mae_zoom, 'r')\n", 283 | "plt.plot(epochs_zoom, loss_zoom, 'b')\n", 284 | "plt.title('MAE and Loss')\n", 285 | "plt.xlabel(\"Epochs\")\n", 286 | "plt.ylabel(\"Accuracy\")\n", 287 | "plt.legend([\"MAE\", \"Loss\"])\n", 288 | "\n", 289 | "plt.figure()" 290 | ], 291 | "execution_count": 0, 292 | "outputs": [] 293 | } 294 | ] 295 | } -------------------------------------------------------------------------------- /4 - Sequences, Time Series and Prediction/Week 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https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/4 - Sequences, Time Series and Prediction/Week 4/Week 4 Quiz.pdf -------------------------------------------------------------------------------- /4 - Sequences, Time Series and Prediction/Week 4/Week 4 Quiz.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MaheshBabu11/DeepLearning.AI-TensorFlow-Developer-Course/3aadc85579898c9fd815eb05012e02a5946d2015/4 - Sequences, Time Series and Prediction/Week 4/Week 4 Quiz.png -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | In the interest of fostering an open and welcoming environment, we as 6 | contributors and maintainers pledge to making participation in our project and 7 | our community a harassment-free experience for everyone, regardless of age, body 8 | size, disability, ethnicity, sex characteristics, gender identity and expression, 9 | level of experience, education, socio-economic status, nationality, personal 10 | appearance, race, religion, or sexual identity and orientation. 11 | 12 | ## Our Standards 13 | 14 | Examples of behavior that contributes to creating a positive environment 15 | include: 16 | 17 | * Using welcoming and inclusive language 18 | * Being respectful of differing viewpoints and experiences 19 | * Gracefully accepting constructive criticism 20 | * Focusing on what is best for the community 21 | * Showing empathy towards other community members 22 | 23 | Examples of unacceptable behavior by participants include: 24 | 25 | * The use of sexualized language or imagery and unwelcome sexual attention or 26 | advances 27 | * Trolling, insulting/derogatory comments, and personal or political 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