DeepLearning_Lesson4.py

# Simple CNN model for CIFAR-10
import numpy
from keras.datasets import cifar10
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.constraints import maxnorm
from keras.optimizers import SGD
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
import keras
import numpy as np
from keras.models import load_model
from keras.callbacks import TensorBoard

K.set_image_dim_ordering('th')

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load data
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
# normalize inputs from 0-255 to 0.0-1.0
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train = X_train / 255.0
X_test = X_test / 255.0
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]

# Create the model

'''Added required layers'''
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(3, 32, 32), padding='same', activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', kernel_constraint=maxnorm(3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', kernel_constraint=maxnorm(3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same', kernel_constraint=maxnorm(3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(1024, activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
epochs = 2
lrate = 0.01
decay = lrate/epochs
tdCallBack = keras.callbacks.TensorBoard(log_dir='./Graph', histogram_freq=0, write_graph=True, write_images=True)
sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
print(model.summary())
# Fit the model
model.fit(X_train[:10000], y_train[:10000], validation_data=(X_test[:1000], y_test[:1000]), epochs=epochs, batch_size=32, callbacks=[tdCallBack])
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print('Test loss', scores[0])
print("Test Accuracy: %.2f%%" % (scores[1]*100))
model.save('./tensor_model.h5')


num_test = [X_test[[i], ] for i in range(4)]

'''How many predictions will need to compare and test with model and data'''
num_test_predictions = [np.argmax(model.predict(test))for test in num_test]

'''First four values in the test data'''
values = [np.argmax(value) for value in y_test[:4]]


'''Output the prediction values compared to the actual values of the model'''
for num in range(4):
    print('Prediction: ', num_test_predictions[num])
    print('Real Value: ', values[num])
    print()