train_old3.py

from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
from keras.layers import Input, Flatten, Dense
from keras.models import Model
import numpy as np
from keras import backend as K
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator

#Get back the convolutional part of a VGG network trained on ImageNet
model_vgg16_conv = VGG16(weights='imagenet', include_top=False)
model_vgg16_conv.summary()

#Create your own input format (here 3x200x200)
input = Input(shape=(3,200,200),name = 'image_input')

# Generate a model with all layers (with top)
vgg16 = VGG16(weights=None, include_top=True)

#Add a layer where input is the output of the  second last layer 
x = Dense(14951, activation='softmax', name='predictions')(vgg16.layers[-2].output)

#Then create the corresponding model 
my_model = Model(input=vgg16.input, output=x)
my_model.summary()
my_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])



# dimensions of our images.
img_width, img_height = 224, 224

train_data_dir = 'training_images'
validation_data_dir = 'validation_images'
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)
else:
    input_shape = (img_width, img_height, 3)

monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, verbose=0, mode='auto')
checkpointer = ModelCheckpoint(filepath="best_weights.hdf5", verbose=0, save_best_only=True) # save best model

# 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')

from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True

my_model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_steps=nb_validation_samples // batch_size,
    callbacks=[monitor,checkpointer], 
    validation_data=validation_generator)

my_model.load_weights('best_weights2.hdf5') # load weights from best model
my_model.save('save_model.h5')