Forked from fchollet/classifier_from_little_data_script_1.py
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Updated to the Keras 2.0 API.
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'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ | |
- created cats/ and dogs/ subfolders inside train/ and validation/ | |
- put the cat pictures index 0-999 in data/train/cats | |
- put the cat pictures index 1000-1400 in data/validation/cats | |
- put the dogs pictures index 12500-13499 in data/train/dogs | |
- put the dog pictures index 13500-13900 in data/validation/dogs | |
So that we have 1000 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 | |
... | |
``` | |
''' | |
from keras.preprocessing.image import ImageDataGenerator | |
from keras.models import Sequential | |
from keras.layers import Conv2D, MaxPooling2D | |
from keras.layers import Activation, Dropout, Flatten, Dense | |
from keras import backend as K | |
# dimensions of our images. | |
img_width, img_height = 150, 150 | |
train_data_dir = 'data/train' | |
validation_data_dir = 'data/validation' | |
nb_train_samples = 2000 | |
nb_validation_samples = 800 | |
epochs = 50 | |
batch_size = 16 | |
if K.image_data_format() == 'channels_first': | |
input_shape = (3, img_width, img_height) | |
print("img data format channels_first") | |
else: | |
input_shape = (img_width, img_height, 3) | |
model = Sequential() | |
model.add(Conv2D(32, (3, 3), input_shape=input_shape)) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Conv2D(32, (3, 3))) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Conv2D(64, (3, 3))) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Flatten()) | |
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']) | |
# this is the augmentation configuration we will use for training | |
train_datagen = ImageDataGenerator( | |
rescale=1. / 255, | |
shear_range=0.2, | |
zoom_range=0.2, | |
horizontal_flip=True) | |
# this is the augmentation configuration we will use for testing: | |
# only rescaling | |
test_datagen = ImageDataGenerator(rescale=1. / 255) | |
train_generator = train_datagen.flow_from_directory( | |
train_data_dir, | |
target_size=(img_width, img_height), | |
batch_size=batch_size, | |
class_mode='binary') | |
print("train_generator") | |
validation_generator = test_datagen.flow_from_directory( | |
validation_data_dir, | |
target_size=(img_width, img_height), | |
batch_size=batch_size, | |
class_mode='binary') | |
print("validation_generator") | |
model.fit_generator( | |
train_generator, | |
steps_per_epoch=nb_train_samples // batch_size, | |
epochs=epochs, | |
validation_data=validation_generator, | |
validation_steps=nb_validation_samples // batch_size) | |
print("fit_generator") | |
model.save('first_model.h5') | |
model.save_weights('first_weights.h5') | |
print("saved model and weights") |
What is the use of test_datagen, when we are using it ? I have gone through the prediction part also https://gist.github.com/ritazh/a7c88875053c1106e407300fc4f1d8d6 but here also I cant find it .
Hello,
how would i proceed if i would've one type of Image that i would like to classify. If the input image reaches 80 % similarity with the type of image i seek to find it would get classified as one of these images otherwise it won't?
For an example, i have 500 images i really like and have 10000 images which i don't know. How would i solve this problem with an CNN?
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Thanks @ritazh . This code and prediction code works perfectly.
I already know deep learning by auditing CNN course by Fei-Fei Li - Sandford Uni, but for Keras, I'm totally newcomers. This code is just really helpful :)
By the way, I also run code from this github and successfully generate model_file.h5 (without weight_file.h5), but when I use that model_file, I get error "No model found in config file". Once I change using your model_file.h5 generated using this code, it works perfectly. Do you know why?
Thank you in advance :)