Question

I have problem with my image classification model using keras. This is the code which have binary class.

tried to set the number of images in datasets as equal.

this is a code for Keras model


train_data_dir = 'path'
validation_data_dir = 'path'
nb_train_samples = 2000
nb_validation_samples = 800
epochs = 10
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(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])


train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)


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')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

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.load_weights('second_try.h5')

and the model saved well. so I run the test code

from keras.models import load_model
from keras.preprocessing import image
import numpy as np

# dimensions of our images
img_width, img_height = 150, 150

# load the model we saved
model = load_model('modelpath')
model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

#predicting multiple images at once
img = image.load_img('imgpath', target_size=(img_width, img_height))

x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)

y = image.img_to_array(img)
y = np.expand_dims(y, axis=0)

images = np.vstack([x, y])
classes = model.predict_classes(images, batch_size=10)
print(classes)

and both images from different classes are printed as 1. Why does this happening?

Answer 1

You should be saving the model or model weights after your training, rather than doing load_weights after fit(). So, 2 ways to do this

After model.fit(), do model.save_weights('second_try.hdf5') which only saves weights. To load the weights, you should first compile your model, and then call load_weights on the model as model.load_weights('second_try.hdf5')
After model.fit(), do model.save('model.hdf5') which saves the weights and the model structure to a single HDF5 file. Then you can use that HDF5 file with load() to reconstruct the whole model, including weights. model = load_model('model.hdf5')

Also, do check if your test data is being prepared correctly. As your training loop uses a generator to prepare the data, you should use a similar generator for preparing the test data as well.

Image Classification using keras predict same output for different classes

1 个答案: