resnet

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications.resnet import ResNet50

def load_train(path):
    datagen = ImageDataGenerator(rescale=1./255, horizontal_flip=True, vertical_flip=True)

    train_datagen_flow = datagen.flow_from_directory(
    path,
    target_size=(150, 150),
    batch_size=16,
    class_mode='sparse',
    seed=12345)

    return train_datagen_flow

def create_model(input_shape):

    optimizer = Adam(lr=0.0001)

    backbone = ResNet50(input_shape=(150, 150, 3),
                    weights='/datasets/keras_models/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
                    include_top=False)


    model = Sequential()
    model.add(backbone)
    model.add(GlobalAveragePololing2D())

    model.add(Dense(units=12, activation='softmax'))
    model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy',
              metrics=['acc'])

    return model

def train_model(model, train_data, test_data, batch_size=None, epochs=3,
               steps_per_epoch=None, validation_steps=None):

    model.fit(train_data, validation_data=test_data, batch_size=batch_size, epochs=epochs,
              steps_per_epoch=steps_per_epoch, validation_steps=validation_steps, verbose=2, shuffle=True)

    return model