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In [1]:



    


import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D, AveragePooling2D
from keras.optimizers import Adam
import glob
from PIL import Image

import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
from keras.layers.core import Flatten, Dense, Dropout, Lambda



    


















    






Using TensorFlow backend.

















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def plots(ims, figsize=(12,6), rows=1, interp=False, titles=None):
    if type(ims[0]) is np.ndarray:
        ims = np.array(ims).astype(np.uint8)
        if (ims.shape[-1] != 3):
            ims = ims.transpose((0,2,3,1))
    f = plt.figure(figsize=figsize)
    for i in range(len(ims)):
        sp = f.add_subplot(rows, len(ims)//rows, i+1)
        sp.axis('Off')
        if titles is not None:
            sp.set_title(titles[i], fontsize=16)
        plt.imshow(ims[i], interpolation=None if interp else 'none')



    











In [3]:



    


from keras.preprocessing import image

BATCH_SIZE = 64
PATH="data/sample/"

def get_fit_sample():
    gen = image.ImageDataGenerator()
    sample_batches = gen.flow_from_directory(PATH+'valid', target_size=(224,224), 
            class_mode='categorical', shuffle=False, batch_size=200)
    imgs, labels = next(sample_batches)
    return imgs

gen = image.ImageDataGenerator(featurewise_std_normalization=True)
gen.fit(get_fit_sample())
val_batches = gen.flow_from_directory(PATH+'valid', target_size=(224,224), 
            class_mode='categorical', shuffle=True, batch_size=BATCH_SIZE)

gen = image.ImageDataGenerator(featurewise_std_normalization=True, horizontal_flip=True, channel_shift_range=100, zoom_range=0.5)
gen.fit(get_fit_sample())
batches = gen.flow_from_directory(PATH+'train', target_size=(224,224), 
            class_mode='categorical', shuffle=True, batch_size=BATCH_SIZE)

#imgs,labels = next(batches)
#plots(imgs[:2])



    


















    






Found 200 images belonging to 2 classes.
Found 200 images belonging to 2 classes.
Found 200 images belonging to 2 classes.
Found 200 images belonging to 2 classes.

















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In [4]:



    


CLASSES = 2
INPUT_SHAPE = (224,224,3)
model = Sequential()
    
# Block 1
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', input_shape=INPUT_SHAPE))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool'))

# Block 2
model.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1'))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool'))

# Block 3
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1'))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2'))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool'))

# Block 4
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1'))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2'))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool'))

# Block 5
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1'))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2'))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool'))

# Classification block
model.add(Flatten(name='flatten'))
model.add(Dense(4096, activation='relu', name='fc1'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu', name='fc2'))
model.add(Dropout(0.5))
model.add(Dense(CLASSES, activation='softmax', name='predictions'))

from keras.optimizers import SGD
sgd = SGD(lr=0.01, decay=0.0005, momentum=0.9, nesterov=False)


model.compile(optimizer=sgd, loss='mean_squared_error', metrics=['accuracy'])



    











In [5]:



    


%%time
hist = model.fit_generator(batches,  steps_per_epoch=10, epochs=10, validation_data=val_batches, validation_steps=10)
 
model.save('ConvNet-D-vgg16-sample.h5')

# http://qiita.com/TypeNULL/items/4e4d7de11ab4361d6085
loss = hist.history['loss']
val_loss = hist.history['val_loss']
nb_epoch = len(loss)
plt.plot(range(nb_epoch), loss, marker='.', label='loss')
plt.plot(range(nb_epoch), val_loss, marker='.', label='val_loss')
plt.legend(loc='best', fontsize=10)
plt.grid()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.show()



    


















    






Epoch 1/10
10/10 [==============================] - 17s - loss: 0.2498 - acc: 0.5278 - val_loss: 0.2498 - val_acc: 0.5360
Epoch 2/10
10/10 [==============================] - 10s - loss: 0.2503 - acc: 0.4965 - val_loss: 0.2501 - val_acc: 0.4602
Epoch 3/10
10/10 [==============================] - 11s - loss: 0.2504 - acc: 0.4817 - val_loss: 0.2507 - val_acc: 0.4716
Epoch 4/10
10/10 [==============================] - 10s - loss: 0.2507 - acc: 0.5048 - val_loss: 0.2527 - val_acc: 0.4697
Epoch 5/10
10/10 [==============================] - 11s - loss: 0.2509 - acc: 0.4881 - val_loss: 0.2504 - val_acc: 0.4716
Epoch 6/10
10/10 [==============================] - 10s - loss: 0.2497 - acc: 0.5185 - val_loss: 0.2494 - val_acc: 0.5170
Epoch 7/10
10/10 [==============================] - 11s - loss: 0.2501 - acc: 0.4904 - val_loss: 0.2496 - val_acc: 0.5417
Epoch 8/10
10/10 [==============================] - 10s - loss: 0.2501 - acc: 0.5210 - val_loss: 0.2500 - val_acc: 0.4621
Epoch 9/10
10/10 [==============================] - 11s - loss: 0.2503 - acc: 0.4709 - val_loss: 0.2493 - val_acc: 0.5246
Epoch 10/10
10/10 [==============================] - 10s - loss: 0.2517 - acc: 0.4457 - val_loss: 0.2505 - val_acc: 0.4716










    

















    






CPU times: user 1min 46s, sys: 13.3 s, total: 1min 59s
Wall time: 1min 59s

















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Content source: ikeyasu/til

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