spatial_train_model.py

from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.models import Model
from tensorflow.keras.models import Model
from tensorflow.keras.layers import GlobalAveragePooling2D, Dense
from tensorflow.keras.optimizers import SGD, Adam

def get_model(data, weights='imagenet'):

    # model
    base_model = InceptionV3(weights=weights, include_top=False)

    x = base_model.output

    # global average pooling
    x = GlobalAveragePooling2D()(x)

    # dense
    x = Dense(1024, activation='relu')(x)

    # and a logistic layer
    predictions = Dense(len(data.classes), activation='softmax')(x)

    # this is the model we will train
    model = Model(inputs=base_model.input, outputs=predictions)

    return model

def freeze_all_but_top(model):
    """Used to train just the top layers of the model."""
    # first: train only the top layers (which were randomly initialized)
    # i.e. freeze all convolutional InceptionV3 layers
    for layer in model.layers[:-2]:
        layer.trainable = False

    # compile the model (should be done *after* setting layers to non-trainable)
    model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

    return model

def freeze_all_but_mid_and_top(model):
    """After we fine-tune the dense layers, train deeper."""
    # we chose to train the top 2 inception blocks, i.e. we will freeze
    # the first 172 layers and unfreeze the rest:
    for layer in model.layers[:172]:
        layer.trainable = False
    for layer in model.layers[172:]:
        layer.trainable = True

    # we need to recompile the model for these modifications to take effect
    # we use SGD with a low learning rate
    model.compile(
        optimizer=SGD(lr=0.0001, momentum=0.9),
        loss='categorical_crossentropy',
        metrics=['accuracy', 'top_k_categorical_accuracy'])

    return model