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train.py
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train.py
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'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
In our example we will be using data that can be downloaded at:
https://www.kaggle.com/tongpython/cat-and-dog
In our setup, it expects:
- a data/ folder
- train/ and validation/ subfolders inside data/
- cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-X in data/train/cats
- put the cat pictures index 1000-1400 in data/validation/cats
- put the dogs pictures index 0-X in data/train/dogs
- put the dog pictures index 1000-1400 in data/validation/dogs
We have X training examples for each class, and 400 validation examples
for each class. In summary, this is our directory structure:
```
data/
train/
dogs/
dog001.jpg
dog002.jpg
...
cats/
cat001.jpg
cat002.jpg
...
validation/
dogs/
dog001.jpg
dog002.jpg
...
cats/
cat001.jpg
cat002.jpg
...
```
'''
import numpy as np
import sys
import os
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dropout, Flatten, Dense
from tensorflow.keras import applications
from tensorflow.keras.callbacks import CSVLogger
from tqdm.keras import TqdmCallback
pathname = os.path.dirname(sys.argv[0])
path = os.path.abspath(pathname)
# dimensions of our images.
img_width, img_height = 150, 150
top_model_weights_path = 'model.weights.h5'
train_data_dir = os.path.join('data', 'train')
validation_data_dir = os.path.join('data', 'validation')
cats_train_path = os.path.join(path, train_data_dir, 'cats')
nb_train_samples = 2 * len([name for name in os.listdir(cats_train_path)
if os.path.isfile(
os.path.join(cats_train_path, name))])
nb_validation_samples = 800
epochs = 10
batch_size = 10
def save_bottlebeck_features():
datagen = ImageDataGenerator(rescale=1. / 255)
# build the VGG16 network
model = applications.VGG16(include_top=False, weights='imagenet')
generator = datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode=None,
shuffle=False)
bottleneck_features_train = model.predict(
generator, nb_train_samples // batch_size)
np.save(open('bottleneck_features_train.npy', 'wb'),
bottleneck_features_train)
generator = datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode=None,
shuffle=False)
bottleneck_features_validation = model.predict(
generator, nb_validation_samples // batch_size)
np.save(open('bottleneck_features_validation.npy', 'wb'),
bottleneck_features_validation)
def train_top_model():
train_data = np.load(open('bottleneck_features_train.npy', 'rb'))
train_labels = np.array(
[0] * (int(nb_train_samples / 2)) + [1] * (int(nb_train_samples / 2)))
validation_data = np.load(open('bottleneck_features_validation.npy', 'rb'))
validation_labels = np.array(
[0] * (int(nb_validation_samples / 2)) +
[1] * (int(nb_validation_samples / 2)))
model = Sequential()
model.add(Flatten(input_shape=train_data.shape[1:]))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',
loss='binary_crossentropy', metrics=['accuracy'])
model.fit(train_data, train_labels,
epochs=epochs,
batch_size=batch_size,
validation_data=(validation_data, validation_labels),
verbose=0,
callbacks=[TqdmCallback(), CSVLogger("metrics.csv")])
model.save_weights(top_model_weights_path)
save_bottlebeck_features()
train_top_model()