In [1]:
import keras
In [53]:
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
In [54]:
import glob
dog_filenames = glob.glob('DogsVsCats-kaggle/train/dogs/*.jpg')
cat_filenames = glob.glob('DogsVsCats-kaggle/train/cats/*.jpg')
In [72]:
# dimensions of our images.
img_width, img_height = 150, 150
def load_images(filenames, target_size):
w, h = target_size
imgs = np.empty((len(filenames), 3, w, h))
for i in range(len(dog_filenames)):
imgs[i] = img_to_array(load_img(filenames[i], target_size=target_size))
return imgs
In [74]:
np.save('dogs.npy', load_images(dog_filenames, (img_width, img_height)))
In [75]:
np.save('cats.npy', load_images(cat_filenames, (img_width, img_height)))
In [76]:
dogs = np.load('dogs.npy', mmap_mode='r')
cats = np.load('cats.npy', mmap_mode='r')
In [89]:
dog_feats = model.predict(dogs[:10])
In [92]:
dog_feats = dog_feats.reshape((len(dog_feats), -1))
In [93]:
cat_feats = model.predict(cats[:10]).reshape((10, -1))
In [94]:
%matplotlib inline
import matplotlib.pyplot as plt
plt.matshow(dog_feats)
Out[94]:
In [6]:
datagen = ImageDataGenerator(
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
img = load_img('DogsVsCats-kaggle/train/cats/cat.0.jpg') # this is a PIL image
x = img_to_array(img) # this is a Numpy array with shape (3, 150, 150)
x = x.reshape((1,) + x.shape) # this is a Numpy array with shape (1, 3, 150, 150)
# the .flow() command below generates batches of randomly transformed images
# and saves the results to the `preview/` directory
i = 0
for batch in datagen.flow(x, batch_size=1,
save_to_dir='preview', save_prefix='cat', save_format='jpeg'):
i += 1
if i > 20:
break # otherwise the generator would loop indefinitely
In [7]:
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
model = Sequential()
model.add(Convolution2D(32, 3, 3, input_shape=(3, 150, 150)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# the model so far outputs 3D feature maps (height, width, features)
model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
In [39]:
import os
import h5py
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.layers import Activation, Dropout, Flatten, Dense
# path to the model weights file.
weights_path = 'vgg16_weights.h5'
top_model_weights_path = 'bottleneck_fc_model.h5'
train_data_dir = 'DogsVsCats-kaggle/train'
validation_data_dir = 'DogsVsCats-kaggle/validation'
nb_train_samples = 2000
nb_validation_samples = 2000
def vgg16_model():
# build the VGG16 network
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=(3, img_width, img_height)))
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# load the weights of the VGG16 networks
# (trained on ImageNet, won the ILSVRC competition in 2014)
# note: when there is a complete match between your model definition
# and your weight savefile, you can simply call model.load_weights(filename)
assert os.path.exists(weights_path), 'Model weights not found (see "weights_path" variable in script).'
f = h5py.File(weights_path)
for k in range(f.attrs['nb_layers']):
if k >= len(model.layers):
# we don't look at the last (fully-connected) layers in the savefile
break
g = f['layer_{}'.format(k)]
weights = [g['param_{}'.format(p)] for p in range(g.attrs['nb_params'])]
model.layers[k].set_weights(weights)
f.close()
return model
In [45]:
model = vgg16_model()
model.build()
In [95]:
f = h5py.File(weights_path)
In [99]:
list(f['layer_0'])
Out[99]:
In [ ]:
model
In [ ]:
model.predict()
In [52]:
model.model.layers[-1].output
Out[52]:
In [10]:
def save_bottlebeck_features():
datagen = ImageDataGenerator(rescale=1./255)
model = vgg16_model()
print('Model loaded.')
generator = datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode=None,
shuffle=False)
bottleneck_features_train = model.predict_generator(generator, nb_train_samples)
np.save('bottleneck_features_train.npy', bottleneck_features_train)
generator = datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode=None,
shuffle=False)
bottleneck_features_validation = model.predict_generator(generator, nb_validation_samples)
np.save('bottleneck_features_validation.npy', bottleneck_features_validation)
save_bottlebeck_features()
In [85]:
np.load('bottleneck_features_train.npy', mmap_mode='r').shape
Out[85]:
In [13]:
from IPython.display import Image
In [29]:
import itertools
gen = ImageDataGenerator(rescale=1/255).flow_from_directory(train_data_dir, target_size=(img_width, img_height), batch_size=10, class_mode=None, shuffle=False)
imgs = next(gen)
In [38]:
array_to_img(imgs[8])
Out[38]:
In [ ]: