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create_model.py
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create_model.py
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# imports
import matplotlib.pyplot as plt
import PIL
import tensorflow as tf
import numpy as np
import os
import json
from tensorflow.python.keras.models import Model, Sequential, load_model
from tensorflow.python.keras.layers import Dense, Flatten, Dropout,GlobalAveragePooling2D,Input
from tensorflow.python.keras.applications import VGG16
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.resnet50 import ResNet50
#from tensorflow.python.keras.applications import preprocess_input, decode_predictions
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.optimizers import Adam, RMSprop, Adadelta
def finetune_vgg16_model(base_model, transfer_layer, x_trainable, dropout, fc_layers, num_classes, new_weights = ""):
# number of freezed layers: 0 (by a default)
freezed_layers = 0
if x_trainable != "all":
if x_trainable == 0:
for layer in base_model.layers:
layer.trainable = False
freezed_layers = freezed_layers + 1
else:
for layer in base_model.layers[:-x_trainable]:
layer.trainable = False
freezed_layers = freezed_layers + 1
all_layers = len(base_model.layers)
print("Number of all layers in a feature-extractor part of model: {}.".format(all_layers))
print("Number of freezed (untrainable) layers in a feature-extractor part of model: {}.".format(freeze))
# adding custom layers to the classification part of a model
x = transfer_layer.output
#x = Dense(num_classes, activation='softmax')
x = Flatten()(x)
for fc in fc_layers:
# New FC layer, random init
x = Dense(fc, activation='relu')(x)
x = Dropout(dropout)(x)
# New softmax layer
predictions = Dense(num_classes, activation='softmax')(x)
finetune_model = Model(inputs=base_model.input, outputs=predictions)
if new_weights != "":
finetune_model.load_weights(new_weights)
return finetune_model
def finetune_resnet50_model(base_model, transfer_layer, x_trainable, dropout, fc_layers, num_classes, new_weights = ""):
# number of freezed layers: 0 (by a default)
freezed_layers = 0
if x_trainable != "all":
if x_trainable == 0:
for layer in base_model.layers:
layer.trainable = False
freezed_layers = freezed_layers + 1
else:
for layer in base_model.layers[:-x_trainable]:
layer.trainable = False
freezed_layers = freezed_layers + 1
all_layers = len(base_model.layers)
print("Number of all layers in a feature-extractor part of model: {}.".format(all_layers))
print("Number of freezed (untrainable) layers in a feature-extractor part of model: {}.".format(freeze))
# adding custom layers to the classification part of a model
x = transfer_layer.output
x = GlobalAveragePooling2D()(x)
predictions = Dense(num_classes, activation='softmax')(x)
finetune_model = Model(inputs=base_model.input, outputs=predictions)
if new_weights != "":
finetune_model.load_weights(new_weights)
return finetune_model
def finetune_inceptionv3(base_model, transfer_layer, x_trainable, dropout, fc_layers, num_classes, new_weights = ""):
# number of freezed layers: 0 (by a default)
freezed_layers = 0
if x_trainable != "all":
if x_trainable == 0:
for layer in base_model.layers:
layer.trainable = False
freezed_layers = freezed_layers + 1
else:
for layer in base_model.layers[:-x_trainable]:
layer.trainable = False
freezed_layers = freezed_layers + 1
all_layers = len(base_model.layers)
print("Number of all layers in a feature-extractor part of model: {}.".format(all_layers))
print("Number of freezed (untrainable) layers in a feature-extractor part of model: {}.".format(freezed_layers))
# adding custom layers to the classification part of a model
x = transfer_layer.output
x = GlobalAveragePooling2D(name='avg_pool')(x)
x = Dropout(dropout)(x)
predictions = Dense(num_classes, activation='softmax')(x)
finetune_model = Model(inputs=base_model.input, outputs=predictions)
if new_weights != "":
finetune_model.load_weights(new_weights)
return finetune_model
if __name__ == "__main__":
base_model = InceptionV3(include_top=False, weights = 'imagenet', input_shape=(299,299,3))
input_shape = base_model.layers[0].output_shape[1:3]
transfer_layer = base_model.get_layer(index=-1)
print(transfer_layer)
new_model = finetune_inceptionv3(base_model, transfer_layer, "all", 0.5, [1024, 1024], 196)
optimizer = Adam(lr=0.000001)
#new_model.load_weights('../saved_models/20190530_1112/weights.best.hdf5')
new_model.compile(optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
print(new_model.summary())
for layer in new_model.layers: print(layer, layer.trainable)