In [4]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.optimizers import Adam
from keras.layers.normalization import BatchNormalization
from keras.utils import np_utils
from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D, GlobalAveragePooling2D
from keras.layers.advanced_activations import LeakyReLU
from keras.preprocessing.image import ImageDataGenerator
np.random.seed(25)
In [5]:
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print("X_train original shape", X_train.shape)
print("y_train original shape", y_train.shape)
print("X_test original shape", X_test.shape)
print("y_test original shape", y_test.shape)
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plt.imshow(X_train[0], cmap='gray')
plt.title('Class '+ str(y_train[0]))
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In [7]:
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train/=255
X_test/=255
X_train.shape
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In [9]:
number_of_classes = 10
Y_train = np_utils.to_categorical(y_train, number_of_classes)
Y_test = np_utils.to_categorical(y_test, number_of_classes)
y_train[0], Y_train[0]
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In [10]:
# Three steps to Convolution
# 1. Convolution
# 2. Activation
# 3. Polling
# Repeat Steps 1,2,3 for adding more hidden layers
# 4. After that make a fully connected network
# This fully connected network gives ability to the CNN
# to classify the samples
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(28,28,1)))
model.add(Activation('relu'))
BatchNormalization(axis=-1)
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
BatchNormalization(axis=-1)
model.add(Conv2D(64,(3, 3)))
model.add(Activation('relu'))
BatchNormalization(axis=-1)
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
# Fully connected layer
BatchNormalization()
model.add(Dense(512))
model.add(Activation('relu'))
BatchNormalization()
model.add(Dropout(0.2))
model.add(Dense(10))
# model.add(Convolution2D(10,3,3, border_mode='same'))
# model.add(GlobalAveragePooling2D())
model.add(Activation('softmax'))
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model.summary()
In [12]:
model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])
In [13]:
gen = ImageDataGenerator(rotation_range=8, width_shift_range=0.08, shear_range=0.3,
height_shift_range=0.08, zoom_range=0.08)
test_gen = ImageDataGenerator()
In [14]:
train_generator = gen.flow(X_train, Y_train, batch_size=64)
test_generator = test_gen.flow(X_test, Y_test, batch_size=64)
In [15]:
# model.fit(X_train, Y_train, batch_size=128, nb_epoch=1, validation_data=(X_test, Y_test))
model.fit_generator(train_generator, steps_per_epoch=60000//64, epochs=5,
validation_data=test_generator, validation_steps=10000//64)
Out[15]:
In [16]:
score = model.evaluate(X_test, Y_test)
print()
print('Test accuracy: ', score[1])
In [18]:
predictions = model.predict_classes(X_test)
predictions = list(predictions)
actuals = list(y_test)
sub = pd.DataFrame({'Actual': actuals, 'Predictions': predictions})
sub.to_csv('./output_cnn.csv', index=False)
Pseudo Labelling¶
In [19]:
class MixIterator(object):
def __init__(self, iters):
self.iters = iters
self.N = sum([it.n for it in self.iters])
def reset(self):
for it in self.iters: it.reset()
def __iter__(self):
return self
def __next__(self, *args, **kwargs):
nexts = [next(it) for it in self.iters]
n0 = np.concatenate([n[0] for n in nexts])
n1 = np.concatenate([n[1] for n in nexts])
return (n0, n1)
In [20]:
predictions = model.predict(X_test, batch_size=64)
predictions[:5]
Out[20]:
In [21]:
# gen = ImageDataGenerator()
batches = gen.flow(X_train, Y_train, batch_size=48)
test_batches = test_gen.flow(X_test, predictions, batch_size=16)
In [22]:
mi = MixIterator([batches, test_batches])
mi.N
Out[22]:
In [23]:
model.fit_generator(mi, steps_per_epoch=mi.N//64, epochs=5, validation_data=(X_test, Y_test))
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