I’ve just run my first CNN project based on The Ultimate Beginner’s Guide to Deep Learning in Python. It’s a simple classifier of MNIST dataset and it works perfectly. I’m amazed and really pleased to understand almost the whole conception. Next step would be a development of a classifier on a more complex dataset.
#####Ref: https://elitedatascience.com/keras-tutorial-deep-learning-in-python##
###############################################################################
########################Import libraries and modules###########################
from keras.datasets import fashion_mnist
import numpy as np
from keras.models import Sequential
## Common NN layers
from keras.layers import Dense, Dropout, Activation, Flatten
## CNN layers
from keras.layers.convolutional import Conv2D
from keras.layers import MaxPooling2D
## Utilities to transform data
from keras.utils import np_utils
###############################################################################
######################## Load image data from MNIST############################
from keras.datasets import mnist
(X_train, Y_train),(X_test, Y_test) = mnist.load_data()
## Look at some data
print (X_train.shape)
# (60000, 28, 28)
from matplotlib import pyplot as plt
plt.imshow(X_train[0])
###############################################################################
########################Preprocess input data for Keras########################
## Transform the dataset from (n, width, height) to (n, depth, width, height)
depth = 1 # for RGB photos depth = 3
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1],X_train.shape[2], depth)
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1],X_test.shape[2], depth)
## Look again at the data
print (X_train.shape)
# (60000, 1, 28, 28)
## Convert the datatype to float32 and normalize the data
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255
###############################################################################
########################Preprocess class labels for Keras######################
## Look at the shape of the class label data
print (Y_train.shape)
#(60000,)
print (Y_train[:10])
# [5 0 4 1 9 2 1 3 1 4]
## Convert 1-dimensional class arrays to 10-dimensional class matrices
num_classes = 10
Y_train = np_utils.to_categorical(Y_train, num_classes)
Y_test = np_utils.to_categorical(Y_test, num_classes)
## Look again at the shape of the class label data
print (Y_train.shape)
# (60000, 10)
print (Y_train[:10][0])
#[ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
###############################################################################
########################Define model architecture##############################
model = Sequential()
## Input layer
## Input: 28x28 images with 1 channel
## This applies 32 convolution filters of size 3x3 each
model.add(Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=(28,28,1)))
## Look at the output shape
print (model.output_shape)
# (None, 32, 26, 26)
## Other layers
model.add(Conv2D(32, (3, 3), activation='relu'))
## Reduce the number of parameters in the model
model.add(MaxPooling2D(pool_size=(2,2)))
## Regularize the model in order to prevent overfitting
model.add(Dropout(0.25))
## Add a fully connected layer and then the output layer
## Flatten the weights from the Convolution layers(made 1-dimensional)...
## ...before passing them to the fully connected Dense layer
model.add(Flatten())
# 128 = output size of this Dense layer
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
# 10 = output size of this Dense layer
model.add(Dense(10, activation='softmax'))
###############################################################################
########################Compile model##########################################
## Compile the model & ...
model.compile(loss ='categorical_crossentropy',
optimizer = 'adam' , # optimizer: SGD, Adam, etc.
metrics = ['accuracy'] )
## Fit model on training data
model.fit(X_train, Y_train,
batch_size = 32,
epochs = 10,
verbose =1)
###############################################################################
########################Evaluate model on test data############################
score = model.evaluate(X_test, Y_test, verbose = 0)
print(score)
Categories: Technical
Tags: AI, CNN, Deep Learning, Project