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In [620]:



    


import glob
import numpy as np
import PIL
from PIL import Image
from collections import Counter
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD
from keras.utils import np_utils
from sklearn.model_selection import StratifiedShuffleSplit, train_test_split
from sklearn.metrics import accuracy_score

Understand the image data





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cat_im = Image.open("data/train/cat.0.jpg")
dog_im = Image.open("data/train/dog.0.jpg")



    











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cat_im



    


















    
Out[3]:
























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dog_im



    


















    
Out[4]:
























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cat_im.size



    


















    
Out[10]:








(500, 374)

















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cat_array = np.array(cat_im.getdata())



    











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cat_array.shape



    


















    
Out[9]:








(187000, 3)

Keras example input data for CNN





In [361]:



    


from keras.datasets import cifar10



    











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(X_train, y_train), (X_test, y_test) = cifar10.load_data()



    











In [363]:



    


# 3 RBG, 32*32 Image
X_train.shape



    


















    
Out[363]:








(50000, 3, 32, 32)

Process the images





In [365]:



    


# need to resize image so that numpy concat works
resized_cat = cat_im.resize((100, 100))
resized_cat



    


















    
Out[365]:
























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np.array(resized_cat).shape



    


















    
Out[367]:








(100, 100, 3)

















In [369]:



    


PIL.Image.fromarray(np.array(resized_cat).T[1])



    


















    
Out[369]:
























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def convert_image_to_data(image):
    image_resized = Image.open(image).resize((100, 100))
    cat_array = np.array(image_resized).T
    return cat_array



    











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cat_files = glob.glob("data/train/cat*")
dog_files = glob.glob("data/train/dog*")



    











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cat_list = []
for i in cat_files[:10]:
    cat_list.append(convert_image_to_data(i))



    











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dog_list = []
for i in dog_files[:10]:
    dog_list.append(convert_image_to_data(i))



    











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y_cat = np.zeros(len(cat_list))



    











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y_dog = np.ones(len(dog_list))



    











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X = np.concatenate([cat_list, dog_list])



    











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# Cast as float32 type
X = X.astype("float32")



    











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X[:2]



    


















    
Out[480]:








array([[[[ 204.,  204.,  204., ...,  159.,  155.,  152.],
         [ 210.,  210.,  210., ...,  161.,  157.,  154.],
         [ 213.,  213.,  213., ...,  164.,  160.,  158.],
         ..., 
         [ 246.,  247.,  248., ...,    3.,    3.,    2.],
         [ 246.,  245.,  245., ...,    3.,    3.,    2.],
         [ 241.,  242.,  244., ...,    2.,    3.,    2.]],

        [[ 165.,  165.,  165., ...,  125.,  124.,  121.],
         [ 171.,  171.,  171., ...,  127.,  126.,  123.],
         [ 174.,  174.,  174., ...,  130.,  129.,  127.],
         ..., 
         [ 209.,  212.,  215., ...,    4.,    3.,    2.],
         [ 205.,  207.,  208., ...,    4.,    3.,    2.],
         [ 202.,  203.,  205., ...,    2.,    3.,    2.]],

        [[  88.,   88.,   88., ...,   54.,   57.,   54.],
         [  94.,   94.,   94., ...,   56.,   59.,   56.],
         [  97.,   97.,   97., ...,   59.,   62.,   60.],
         ..., 
         [ 128.,  131.,  138., ...,    0.,    1.,    0.],
         [ 123.,  126.,  128., ...,    0.,    1.,    0.],
         [ 123.,  124.,  126., ...,    0.,    1.,    0.]]],


       [[[  40.,   37.,   39., ...,   21.,   24.,   30.],
         [  41.,   33.,   33., ...,   20.,   19.,   21.],
         [  40.,   35.,   36., ...,   20.,   21.,   25.],
         ..., 
         [  92.,  100.,   89., ...,   80.,   70.,  104.],
         [ 187.,  180.,  179., ...,   77.,   50.,   28.],
         [ 199.,  201.,  203., ...,   44.,   40.,   46.]],

        [[  45.,   42.,   44., ...,   19.,   22.,   28.],
         [  45.,   37.,   37., ...,   18.,   17.,   19.],
         [  43.,   38.,   39., ...,   18.,   19.,   23.],
         ..., 
         [  96.,  103.,   92., ...,   62.,   52.,   87.],
         [ 185.,  177.,  173., ...,   62.,   35.,   13.],
         [ 196.,  196.,  193., ...,   30.,   27.,   33.]],

        [[  41.,   38.,   40., ...,   20.,   23.,   29.],
         [  48.,   40.,   40., ...,   19.,   18.,   20.],
         [  50.,   45.,   46., ...,   19.,   20.,   24.],
         ..., 
         [  97.,  108.,   99., ...,   60.,   48.,   80.],
         [ 164.,  160.,  159., ...,   59.,   30.,    6.],
         [ 161.,  166.,  166., ...,   27.,   21.,   25.]]]], dtype=float32)

















In [481]:



    


X.shape



    


















    
Out[481]:








(20, 3, 100, 100)

















In [494]:



    


y = np.concatenate([y_cat, y_dog])



    











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# cast as integer
y = y.astype("int")



    











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y



    


















    
Out[497]:








array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

















In [ ]:

Split train data into test/train





In [499]:



    


X.shape



    


















    
Out[499]:








(20, 3, 100, 100)

















In [500]:



    


y.shape



    


















    
Out[500]:








(20,)

















In [581]:



    


sss = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=1)



    











In [582]:



    


for train_index, test_index in sss.split(X, y):
    print("TRAIN:", train_index, "TEST:", test_index)
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]



    


















    






TRAIN: [12  3  4 11 19  2  1 13  9 15 10  7  0  6 14 18] TEST: [16  8 17  5]

















In [583]:



    


y_train



    


















    
Out[583]:








array([1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1])

















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y_test



    


















    
Out[584]:








array([1, 0, 1, 0])

















In [585]:



    


Counter(y_train), Counter(y_test)



    


















    
Out[585]:








(Counter({0: 8, 1: 8}), Counter({0: 2, 1: 2}))

















In [612]:



    


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1, stratify=y)



    











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y_train, y_test



    


















    
Out[613]:








(array([1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1]), array([1, 0, 1, 0]))

















In [614]:



    


Counter(y_train), Counter(y_test)



    


















    
Out[614]:








(Counter({0: 8, 1: 8}), Counter({0: 2, 1: 2}))

Train model and validate it





In [622]:



    


model = Sequential()
# input: 100x100 images with 3 channels -> (3, 100, 100) tensors.
# this applies 32 convolution filters of size 3x3 each.
model.add(Convolution2D(32, 3, 3, border_mode='valid', input_shape=(3, 100, 100)))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Convolution2D(64, 3, 3, border_mode='valid'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
# Note: Keras does automatic shape inference.
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(1))
model.add(Activation('sigmoid'))

sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])



    











In [623]:



    


model.fit(X_train, y_train, batch_size=32, nb_epoch=20)



    


















    






Epoch 1/20
16/16 [==============================] - 0s - loss: 5.0208 - acc: 0.6875
Epoch 2/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 3/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 4/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 5/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 6/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 7/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 8/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 9/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 10/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 11/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 12/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 13/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 14/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 15/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 16/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 17/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 18/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 19/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000
Epoch 20/20
16/16 [==============================] - 0s - loss: 8.0590 - acc: 0.5000










    
Out[623]:








<keras.callbacks.History at 0x124db30f0>

















In [618]:



    


y_test_predict = model.predict(X_test)



    











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accuracy_score(y_test, y_test_predict)



    


















    
Out[621]:








0.5

















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Content source: datitran/Dogs-vs-Cats

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