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EVA_Assign_6A.py
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EVA_Assign_6A.py
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# -*- coding: utf-8 -*-
"""EVA_Assignment_6A.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1jntlvuP8wJyZDb_KiqzAbtBnrMPyDeLa
"""
from keras import backend as K
import time
import matplotlib.pyplot as plt
import numpy as np
# % matplotlib inline
np.random.seed(2017)
from keras.models import Sequential
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers import Activation, Flatten, Dense, Dropout
from keras.layers.normalization import BatchNormalization
from keras.utils import np_utils
from keras.datasets import cifar10
(train_features, train_labels), (test_features, test_labels) = cifar10.load_data()
num_train, img_channels, img_rows, img_cols = train_features.shape
num_test, _, _, _ = test_features.shape
num_classes = len(np.unique(train_labels))
class_names = ['airplane','automobile','bird','cat','deer',
'dog','frog','horse','ship','truck']
fig = plt.figure(figsize=(8,3))
for i in range(num_classes):
ax = fig.add_subplot(2, 5, 1 + i, xticks=[], yticks=[])
idx = np.where(train_labels[:]==i)[0]
features_idx = train_features[idx,::]
img_num = np.random.randint(features_idx.shape[0])
im = features_idx[img_num]
ax.set_title(class_names[i])
plt.imshow(im)
plt.show()
def plot_model_history(model_history):
fig, axs = plt.subplots(1,2,figsize=(15,5))
# summarize history for accuracy
axs[0].plot(range(1,len(model_history.history['acc'])+1),model_history.history['acc'])
axs[0].plot(range(1,len(model_history.history['val_acc'])+1),model_history.history['val_acc'])
axs[0].set_title('Model Accuracy')
axs[0].set_ylabel('Accuracy')
axs[0].set_xlabel('Epoch')
axs[0].set_xticks(np.arange(1,len(model_history.history['acc'])+1),len(model_history.history['acc'])/10)
axs[0].legend(['train', 'val'], loc='best')
# summarize history for loss
axs[1].plot(range(1,len(model_history.history['loss'])+1),model_history.history['loss'])
axs[1].plot(range(1,len(model_history.history['val_loss'])+1),model_history.history['val_loss'])
axs[1].set_title('Model Loss')
axs[1].set_ylabel('Loss')
axs[1].set_xlabel('Epoch')
axs[1].set_xticks(np.arange(1,len(model_history.history['loss'])+1),len(model_history.history['loss'])/10)
axs[1].legend(['train', 'val'], loc='best')
plt.show()
def accuracy(test_x, test_y, model):
result = model.predict(test_x)
predicted_class = np.argmax(result, axis=1)
true_class = np.argmax(test_y, axis=1)
num_correct = np.sum(predicted_class == true_class)
accuracy = float(num_correct)/result.shape[0]
return (accuracy * 100)
train_features = train_features.astype('float32')/255
test_features = test_features.astype('float32')/255
# convert class labels to binary class labels
train_labels = np_utils.to_categorical(train_labels, num_classes)
test_labels = np_utils.to_categorical(test_labels, num_classes)
# Define the model
model = Sequential()
model.add(Convolution2D(48, 3, 3, border_mode='same', input_shape=(32, 32, 3)))
model.add(Activation('relu'))
model.add(Convolution2D(48, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(96, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(96, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(192, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(192, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))#remove
model.add(Activation('relu'))
model.add(Dropout(0.5))#miss place doupout
model.add(Dense(256))#remove
model.add(Activation('relu'))
model.add(Dropout(0.5))#miss place doupout
model.add(Dense(num_classes, activation='softmax'))#remove
# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(zoom_range=0.0,
horizontal_flip=False)
# train the model
start = time.time()
# Train the model
model_info = model.fit_generator(datagen.flow(train_features, train_labels, batch_size = 128),
samples_per_epoch = train_features.shape[0], nb_epoch = 100,
validation_data = (test_features, test_labels), verbose=1)
end = time.time()
print ("Model took %0.2f seconds to train"%(end - start))
# plot model history
plot_model_history(model_info)
# compute test accuracy
print ("Accuracy on test data is: %0.2f"%accuracy(test_features, test_labels, model))
"""# Want to beat val_acc: 0.8364"""
model = Sequential()
model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=(32, 32, 3)))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3,border_mode='same')) #32x32x48
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2))) # 16x16x48
model.add(Dropout(0.25))
model.add(Convolution2D(64, 3, 3, border_mode='same')) #16x15x96
model.add(Activation('relu'))
# model.add(Convolution2D(128, 3, 3,border_mode='same'))#16x16x96
# model.add(Activation('relu'))
# model.add(Dropout(0.25))
model.add(MaxPooling2D(pool_size=(2, 2)))#8x8x96
model.add(Dropout(0.25))
model.add(Convolution2D(64, 3, 3, border_mode='same'))#8x8x64
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(Convolution2D(32, 3, 3,border_mode='same'))#8x8x32
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(Convolution2D(16, 10, 10,border_mode='same'))#8x8x16
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(Convolution2D(10, 10, 10, border_mode='same'))#8x8x10
model.add(Activation('relu'))
model.add(Dropout(0.25))
#taking 10 channel out of 100
model.add(Convolution2D(10, 8))#1x1x10
model.add(Convolution2D(10, 1, 1))#1x1x10
# model.add(Convolution2D(10, 8))#1x1x10
model.add(Flatten())#10
model.add(Activation('softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(zoom_range=0.0,
horizontal_flip=False)
# train the model
start = time.time()
# Train the model
model_info = model.fit_generator(datagen.flow(train_features, train_labels, batch_size = 128),
samples_per_epoch = train_features.shape[0], nb_epoch = 100,
validation_data = (test_features, test_labels), verbose=1)
end = time.time()
print ("Model took %0.2f seconds to train"%(end - start))
# plot model history
plot_model_history(model_info)
# compute test accuracy
print ("Accuracy on test data is: %0.2f"%accuracy(test_features, test_labels, model))
#after reaching val_acc=80 & test_acc = 80 model is little overfiting #8x8x10 added dropout.
model = Sequential()
model.add(Convolution2D(128, 1, 3, border_mode='same', input_shape=(32, 32, 3)))
model.add(Activation('relu'))
model.add(Convolution2D(256, 3, 1,border_mode='same')) #32x32x48
model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2, 2))) # 16x16x48
# model.add(Dropout(0.25))
model.summary()