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incep-resn_v2_more_layers.py
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incep-resn_v2_more_layers.py
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#
# Modified from Keras doc: Fine-tune InceptionV3 on a new set of classes
#Keras blog
from keras.applications.inception_resnet_v2 import InceptionResNetV2
from keras.preprocessing import image
from keras.models import Model, Sequential
from keras.layers import Dense, GlobalAveragePooling2D,Dropout, Flatten
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator
from keras.metrics import top_k_categorical_accuracy, sparse_top_k_categorical_accuracy
from keras import applications
import keras
import numpy as np
import tensorflow as tf
# dimensions of our images.
img_width, img_height = 299, 299
top_model_weights_path = 'bottleneck_fc_model_2.h5'
train_data_dir = '../data/scene_classification/scene_train_images_20170904'
validation_data_dir = '../data/scene_classification/scene_validation_images_20170908'
train_labels = np.load('training_labels.npy')
validation_labels = np.load('validation_labels.npy')
nb_train_samples = len(train_labels)
nb_validation_samples = 7120
epochs = 10
batch_size = 1
def to_one_hot(array):
array=array.astype(np.int)
n_values = np.max(array) + 1
return np.eye(n_values)[array]
def show_layers(model):
for i, layer in enumerate(model.layers):
print(i, layer.name)
def top_3_categorical_accuracy(y_true, y_pred, k=3):
return K.mean(K.in_top_k(y_pred, K.argmax(y_true, axis=-1), k))
def save_features():
datagen = ImageDataGenerator(rescale=1. / 255)
# build the network
base_model = keras.applications.inception_resnet_v2.InceptionResNetV2(weights='imagenet', include_top=False, pooling='avg')
model = Model(inputs=base_model.input, outputs=base_model.get_layer('block8_10').output)
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(
generator, 1 + nb_train_samples // batch_size, verbose=1)
#+1 in order not to lose the last batch
# !!! labels need to be modified accordingly
np.save('conv_features_train.npy', bottleneck_features_train)
#the array will have size (no. of samples, 1536)
#save_validation_features:
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(
generator, 1 + nb_validation_samples // batch_size)
np.save('conv_features_validation.npy', bottleneck_features_validation)
def train_top_model():
np.random.seed(1)
global train_labels
global validation_labels
train_data = np.load('conv_features_train.npy')
#train_labels = np.array([0] * (nb_train_samples / 2) + [1] * (nb_train_samples / 2))
#train_labels have been defined globally
validation_data = np.load('bottleneck_features_validation.npy')
#validation_labels = np.array([0] * (nb_validation_samples / 2) + [1] * (nb_validation_samples / 2))
# array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
base_model = keras.applications.inception_resnet_v2.InceptionResNetV2(weights='imagenet', include_top=False, pooling='avg')
conv_model = Sequential()
conv_model.add(base_model.get_layer(name='conv_7b'))
top_model = Sequential()
#model.add(Flatten(input_shape=train_data.shape[1:]))
top_model.add(Dropout(0.3, input_shape=(1536,)))
top_model.add(Dense(1000, activation='relu'))
top_model.add(Dropout(0.3))
top_model.add(Dense(80, activation='softmax'))
model = Model(inputs=conv.input, outputs=top_model.outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy',top_3_categorical_accuracy])
#keras.optimizers.RMSprop(lr=0.0005)
train_labels=train_labels.reshape((len(train_labels),1))
trainmerge=np.append(train_data,train_labels,axis=1)
validation_labels=validation_labels.reshape((len(validation_labels),1))
validation_data=validation_data[:-20,:]
validationmerge=np.append(validation_data,validation_labels,axis=1)
np.random.shuffle(trainmerge)
np.random.shuffle(validationmerge)
train_data=trainmerge[:,:-1]
train_labels=trainmerge[:,-1]
validation_data=validationmerge[:,:-1]
validation_labels=validationmerge[:,-1]
#validation_labels=K.one_hot(validation_labels,80)
validation_labels=to_one_hot(validation_labels.flatten())
train_labels=to_one_hot(train_labels.flatten())
model.load_weights(top_model_weights_path)
model.fit(train_data, train_labels,
epochs=epochs,
batch_size=batch_size,
validation_data=(validation_data, validation_labels))
model.save_weights(top_model_weights_path)
save_features()
#train_top_model()