diff --git a/Framework/Keras/Keras Assingment/Harsh_Task1/harsh.ipynb b/Framework/Keras/Keras Assingment/Harsh_Task1/harsh.ipynb new file mode 100644 index 0000000..e84a495 --- /dev/null +++ b/Framework/Keras/Keras Assingment/Harsh_Task1/harsh.ipynb @@ -0,0 +1,233 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.utils.np_utils import to_categorical" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.datasets import mnist" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.layers import Dense" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.models import Sequential" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.callbacks import CSVLogger" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "(X_train, Y_train),(X_test, Y_test) = mnist.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = X_train.reshape(X_train.shape[0],784)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "X_test = X_test.reshape(X_test.shape[0], 784)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = X_train / 255\n", + "X_test = X_test / 255" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "Y_train = to_categorical(Y_train)\n", + "Y_test = to_categorical(Y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "model.add(Dense(512,input_dim = 784 ,activation = 'relu'))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "model.add(Dense(10,activation = 'softmax'))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(optimizer = 'adam',loss = 'categorical_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 60000 samples, validate on 10000 samples\n", + "Epoch 1/10\n", + " - 9s - loss: 0.2155 - acc: 0.9375 - val_loss: 0.1092 - val_acc: 0.9667\n", + "Epoch 2/10\n", + " - 8s - loss: 0.0855 - acc: 0.9741 - val_loss: 0.0788 - val_acc: 0.9757\n", + "Epoch 3/10\n", + " - 8s - loss: 0.0547 - acc: 0.9832 - val_loss: 0.0802 - val_acc: 0.9748\n", + "Epoch 4/10\n", + " - 10s - loss: 0.0395 - acc: 0.9875 - val_loss: 0.0900 - val_acc: 0.9727\n", + "Epoch 5/10\n", + " - 11s - loss: 0.0285 - acc: 0.9907 - val_loss: 0.0681 - val_acc: 0.9796\n", + "Epoch 6/10\n", + " - 9s - loss: 0.0215 - acc: 0.9927 - val_loss: 0.0669 - val_acc: 0.9799\n", + "Epoch 7/10\n", + " - 9s - loss: 0.0166 - acc: 0.9949 - val_loss: 0.0857 - val_acc: 0.9759\n", + "Epoch 8/10\n", + " - 8s - loss: 0.0146 - acc: 0.9953 - val_loss: 0.0830 - val_acc: 0.9764\n", + "Epoch 9/10\n", + " - 8s - loss: 0.0107 - acc: 0.9965 - val_loss: 0.0695 - val_acc: 0.9833\n", + "Epoch 10/10\n", + " - 9s - loss: 0.0089 - acc: 0.9972 - val_loss: 0.0844 - val_acc: 0.9795\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X_train,Y_train, validation_data=(X_test,Y_test),epochs = 10, batch_size = 50, verbose = 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "accuracy = model.evaluate(X_test, Y_test, verbose=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy is 97.95\n" + ] + } + ], + "source": [ + "print(\"accuracy is %0.2f\" % (accuracy[-1]*100)) " + ] + }, + { + "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 +} diff --git a/Phase 1/Python assignment/Top95.txt b/Phase 1/Python assignment/Top95.txt new file mode 100644 index 0000000..77a73cf --- /dev/null +++ b/Phase 1/Python assignment/Top95.txt @@ -0,0 +1,95 @@ +said 2762 +one 2008 +prince 1856 +pierre 1753 +now 1242 +natásha 1061 +will 1050 +man 1031 +andrew 1015 +time 891 +face 885 +princess 859 +went 857 +french 847 +eyes 815 +know 806 +old 801 +room 757 +thought 752 +men 747 +chapter 731 +began 708 +see 703 +rostóv 702 +go 701 +came 680 +without 667 +moscow 665 +asked 664 +still 659 +looked 646 +come 646 +well 640 +felt 626 +count 616 +army 615 +first 612 +left 596 +mary 595 +another 591 +something 589 +say 579 +seemed 578 +two 573 +away 572 +nicholas 570 +life 563 +head 558 +little 552 +day 535 +whole 528 +hand 527 +don’t 515 +people 508 +even 503 +yes 503 +long 501 +back 497 +emperor 495 +heard 491 +must 480 +general 468 +way 467 +napoleon 462 +always 461 +saw 461 +look 461 +made 457 +russian 449 +nothing 442 +young 440 +though 435 +countess 434 +kutúzov 430 +suddenly 428 +love 426 +round 418 +knew 407 +right 407 +voice 407 +smile 406 +never 405 +told 405 +officer 402 +moment 400 +took 395 +looking 389 +us 389 +everything 386 +much 385 +sónya 385 +turned 384 +let 375 +quite 374 +tell 373