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rahulkrishnan98 / convMNIST.py
Created January 18, 2019 03:04
model=Sequential()
model.add(Conv2D(32,(3,3),strides=(1,1),padding='same',activation='relu',input_shape=(28,28,1)))
model.add(Flatten())
model.add(Dense(100,activation='relu'))
model.add(Dense(10,activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='nadam', metrics=['accuracy'])
@rahulkrishnan98
rahulkrishnan98 / min-char-rnn.py
Created September 5, 2018 12:52 — forked from karpathy/min-char-rnn.py
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np
# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
@rahulkrishnan98
rahulkrishnan98 / YOLOlog.txt
Created June 21, 2018 07:37
Parsing ./cfg/tiny-yolo-voc.cfg
Parsing cfg/tiny-yolo-voc-1c.cfg
Loading bin/tiny-yolo-voc.weights ...
Successfully identified 63471556 bytes
Finished in 0.010008573532104492s
Building net ...
Source | Train? | Layer description | Output size
-------+--------+----------------------------------+---------------
| | input | (?, 416, 416, 3)
@rahulkrishnan98
rahulkrishnan98 / finalSteps.py
Created June 12, 2018 10:09
model=baseline_model()
model.fit(X_train, y_train,validation_data=(X_test,y_test), epochs=10, batch_size=200,verbose=2)
scores=model.evaluate(X_test, y_test,verbose=0)
@rahulkrishnan98
rahulkrishnan98 / build.py
Created June 12, 2018 09:47
def baseline_model():
model=Sequential()
model.add(Conv2D(32,(5,5),input_shape=(1,28,28),activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128,activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
@rahulkrishnan98
rahulkrishnan98 / normalize.py
Created June 12, 2018 09:43
#normalize
X_train=X_train/255
X_test=X_test/255
#one hot encoding
y_train=np_utils.to_categorical(y_train)
y_test=np_utils.to_categorical(y_test)
num_classes=y_test.shape[1]
@rahulkrishnan98
rahulkrishnan98 / seed.py
Created June 12, 2018 09:38
seed=7
numpy.random.seed(seed)
(X_train, y_train), (X_test, y_test)=mnist.load_data()
X_train= X_train.reshape(X_train.shape[0],1,28,28).astype('float32')
X_test=X_test.reshape(X_test.shape[0],1,28,28).astype('float32')
@rahulkrishnan98
rahulkrishnan98 / CNN.py
Last active June 12, 2018 09:33
import numpy
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
@rahulkrishnan98
rahulkrishnan98 / build.log
Created June 11, 2018 10:07
Microsoft (R) Build Engine version 15.7.179.6572 for .NET Framework
Copyright (C) Microsoft Corporation. All rights reserved.
Build started 11-06-2018 15:29:00.
Project "C:\Users\Rahul\Documents\ELL\ELL\build\ALL_BUILD.vcxproj" on node 1 (default targets).
Project "C:\Users\Rahul\Documents\ELL\ELL\build\ALL_BUILD.vcxproj" (1) is building "C:\Users\Rahul\Documents\ELL\ELL\build\ZERO_CHECK.vcxproj" (2) on node 1 (default targets).
InitializeBuildStatus:
Creating "x64\Release\ZERO_CHECK\ZERO_CHECK.tlog\unsuccessfulbuild" because "AlwaysCreate" was specified.
CustomBuild:
All outputs are up-to-date.
@rahulkrishnan98
rahulkrishnan98 / index.html
Created September 12, 2017 10:19
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Github Battle</title>
</head>
<body>
<div id="app"></div>
</body>
</html>