In [1]:
%%html
<style>
@import url('https://fonts.googleapis.com/css?family=Orbitron|Roboto');
body {background-color: #add8e6;}
a {color: darkblue; font-family: 'Roboto';}
h1 {color: steelblue; font-family: 'Orbitron'; text-shadow: 4px 4px 4px #aaa;}
h2, h3 {color: #483d8b; font-family: 'Orbitron'; text-shadow: 4px 4px 4px #aaa;}
h4 {color: slategray; font-family: 'Roboto';}
span {text-shadow: 4px 4px 4px #ccc;}
div.output_prompt, div.output_area pre {color: #483d8b;}
div.input_prompt, div.output_subarea {color: darkblue;}
div.output_stderr pre {background-color: #add8e6;}
div.output_stderr {background-color: #483d8b;}
</style>
In [2]:
import keras
from keras.datasets import cifar10
# load the pre-shuffled train and test data
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
2. Visualize the First 24 Training Images¶
In [3]:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
fig = plt.figure(figsize=(20,5))
for i in range(36):
ax = fig.add_subplot(3, 12, i + 1, xticks=[], yticks=[])
ax.imshow(np.squeeze(x_train[i]))
3. Rescale the Images by Dividing Every Pixel in Every Image by 255¶
In [4]:
# rescale [0,255] --> [0,1]
x_train = x_train.astype('float32')/255
x_test = x_test.astype('float32')/255
4. Break Dataset into Training, Testing, and Validation Sets¶
In [5]:
from keras.utils import np_utils
# one-hot encode the labels
num_classes = len(np.unique(y_train))
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# break training set into training and validation sets
(x_train, x_valid) = x_train[5000:], x_train[:5000]
(y_train, y_valid) = y_train[5000:], y_train[:5000]
# print shape of training set
print('x_train shape:', x_train.shape)
# print number of training, validation, and test images
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
print(x_valid.shape[0], 'validation samples')
5. Define the Model Architecture¶
In [6]:
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
# define the model
model = Sequential()
model.add(Flatten(input_shape = x_train.shape[1:]))
model.add(Dense(1000, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
6. Compile the Model¶
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# compile the model
model.compile(loss='categorical_crossentropy', optimizer='rmsprop',
metrics=['accuracy'])
7. Train the Model¶
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from keras.callbacks import ModelCheckpoint
# train the model
checkpointer = ModelCheckpoint(filepath='MLP.weights.best.hdf5', verbose=1,
save_best_only=True)
hist = model.fit(x_train, y_train, batch_size=32, epochs=20,
validation_data=(x_valid, y_valid), callbacks=[checkpointer],
verbose=2, shuffle=True)
8. Load the Model with the Best Classification Accuracy on the Validation Set¶
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# load the weights that yielded the best validation accuracy
model.load_weights('MLP.weights.best.hdf5')
9. Calculate Classification Accuracy on Test Set¶
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# evaluate and print test accuracy
score = model.evaluate(x_test, y_test, verbose=0)
print('\n', 'Test accuracy:', score[1])