finding the independent probability in image objects #1
Description
Hi, I have tried with the below an trained got an accuracy 80%
'''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/Val'
nb_train_samples = 4654
nb_validation_samples = 1168
epochs = 1
batch_size = 16
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
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(10))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
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='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical')
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)
model.save('first_sig_try.h5')
and the prediction code is:
from keras.models import load_model
from keras.preprocessing.image import img_to_array, load_img
import numpy as np
img_width = 150
img_height = 150
with open('../weights/output1.txt') as f:
labels = f.read().splitlines()
test_model = load_model('first_sig_try.h5')
img = load_img('/home/rakashi/Desktop/123.jpg',False,target_size=(img_width,img_height))
x = img_to_array(img)
x = np.expand_dims(x, axis=0)
preds = test_model.predict_classes(x)
prob = test_model.predict_proba(x)
preds1 = test_model.predict(x)
for i in xrange(len(preds1[0])):
#if preds1[0][i]:
print labels[i], '-> ', preds1[0][i]
#print labels[int(test_model.predict_classes(np.array([img])))]
but the probability is not coming independently. its coming in the way like this below
Apple -> 0.0
Banana -> 0.0
Broccoli -> 0.0
Burger -> 0.0
Egg -> 0.0
Frenchfry -> 0.0
Hotdog -> 0.0
Pizza -> 1.0
Rice -> 0.0
Strawberry -> 0.0
but i need get the independent probability for each class