KerasTask1done

Framework/Keras/Keras Assingment/HarshalDesai_Task1/HarshalDesai_ModelwithLogs.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Import keras\n",
+    "import keras\n",
+    "#Import the MNIST dataset\n",
+    "from keras.datasets import mnist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Loading dataset into train and test sets\n",
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Normalize the dataset\n",
+    "X_train = keras.utils.normalize(X_train, axis=1)\n",
+    "X_test = keras.utils.normalize(X_test, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import `Sequential` from `keras.models`\n",
+    "from keras.models import Sequential"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Initialize the model\n",
+    "model = keras.models.Sequential()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Adding input layer by flattening the 28x28 pixels image into a vector of size 784 \n",
+    "#Using Flatten()\n",
+    "model.add(keras.layers.Flatten())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import `Dense` from `keras.layers`\n",
+    "from keras.layers import Dense\n",
+    "#Adding hidden layer of size 512 with activation function Relu\n",
+    "model.add(keras.layers.Dense(512, activation='relu'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Adding output layer of size 10 with activation softmax\n",
+    "model.add(keras.layers.Dense(10, activation='softmax'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Compiling the model by using the sgd optimizer\n",
+    "model.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "60000/60000 [==============================] - 13s 221us/step - loss: 0.2341 - acc: 0.9349\n",
+      "Epoch 2/10\n",
+      "60000/60000 [==============================] - 14s 230us/step - loss: 0.2259 - acc: 0.9367\n",
+      "Epoch 3/10\n",
+      "60000/60000 [==============================] - 14s 225us/step - loss: 0.2181 - acc: 0.9385\n",
+      "Epoch 4/10\n",
+      "60000/60000 [==============================] - 10s 171us/step - loss: 0.2111 - acc: 0.9408\n",
+      "Epoch 5/10\n",
+      "60000/60000 [==============================] - 10s 168us/step - loss: 0.2044 - acc: 0.9425\n",
+      "Epoch 6/10\n",
+      "60000/60000 [==============================] - 14s 237us/step - loss: 0.1981 - acc: 0.9440\n",
+      "Epoch 7/10\n",
+      "60000/60000 [==============================] - 14s 235us/step - loss: 0.1922 - acc: 0.9455\n",
+      "Epoch 8/10\n",
+      "60000/60000 [==============================] - 14s 231us/step - loss: 0.1866 - acc: 0.9474\n",
+      "Epoch 9/10\n",
+      "60000/60000 [==============================] - 14s 238us/step - loss: 0.1814 - acc: 0.9490\n",
+      "Epoch 10/10\n",
+      "60000/60000 [==============================] - 14s 239us/step - loss: 0.1764 - acc: 0.9506\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7ff55e87ee48>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Training the model\n",
+    "model.fit(X_train, y_train, epochs=10,batch_size=32, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10000/10000 [==============================] - 1s 64us/step\n",
+      "Loss: 0.18251839239299297\n",
+      "Accuarcy: 94.69999999999999%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Evaluating the model on the test dataset\n",
+    "val_loss, val_acc = model.evaluate(X_test, y_test)\n",
+    "print(\"Loss: \" + str(val_loss))\n",
+    "print(\"Accuarcy: \" + str(100*val_acc) + \"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}