adityajn105

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/mnist",one_hot=True)

n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500 
n_classes = 10
batch_size = 100

adityajn105

"""
Author : Aditya Jain
Contact: [email protected]
"""

import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')

import numpy as np

adityajn105

import numpy as np

from keras.models import Sequential
from keras.layers.core import Dense,Activation,Dropout
from keras.datasets import mnist
from keras.utils import np_utils

no_epochs = 30
batch_size=150 #1000
v_length = 784

adityajn105

# Mathieu Blondel, September 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg
import cvxopt
import cvxopt.solvers

def linear_kernel(x1, x2):
    return np.dot(x1, x2)

adityajn105

import numpy as np

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense,Conv2D,MaxPooling2D
from keras.layers import Flatten,Dropout,Activation

#create a model
model = Sequential()
model.add(Conv2D(64,(5,5),input_shape=(28,28,1),data_format="channels_last",strides=(1,1),padding="valid",activation="relu"))