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odir_model_vgg19.py
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odir_model_vgg19.py
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# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import tensorflow
from tensorflow.keras import models, layers
from tensorflow.keras.optimizers import SGD
from odir_model_base import ModelBase
class Vgg19(ModelBase):
def compile(self):
x = models.Sequential()
trainable = False
# Block 1
layer = layers.Conv2D(input_shape=self.input_shape, filters=64, kernel_size=(3, 3), padding="same",
activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.Conv2D(filters=64, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.MaxPooling2D((2, 2), strides=(2, 2))
layer.trainable = trainable
x.add(layer)
# Block 2
layer = layers.Conv2D(128, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.Conv2D(128, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.MaxPooling2D((2, 2), strides=(2, 2))
layer.trainable = trainable
x.add(layer)
# Block 3
layer = layers.Conv2D(256, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.Conv2D(256, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.Conv2D(256, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.Conv2D(256, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.MaxPooling2D((2, 2), strides=(2, 2))
layer.trainable = trainable
x.add(layer)
# Block 4
layer = layers.Conv2D(512, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.Conv2D(512, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.Conv2D(512, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.Conv2D(512, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.MaxPooling2D((2, 2), strides=(2, 2))
layer.trainable = trainable
x.add(layer)
# Block 5
layer = layers.Conv2D(512, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.Conv2D(512, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.Conv2D(512, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.Conv2D(512, kernel_size=(3, 3), padding="same", activation="relu")
layer.trainable = trainable
x.add(layer)
layer = layers.MaxPooling2D((2, 2), strides=(2, 2))
layer.trainable = trainable
x.add(layer)
layer = layers.Flatten()
layer.trainable = trainable
x.add(layer)
layer = layers.Dense(4096, activation='relu')
layer.trainable = trainable
x.add(layer)
#layer = layers.Dropout(0.5)
#layer.trainable = True
#x.add(layer)
layer = layers.Dense(4096, activation='relu')
layer.trainable = trainable
x.add(layer)
#layer = layers.Dropout(0.5)
#layer.trainable = True
#x.add(layer)
layer = layers.Dense(1000, activation='softmax')
layer.trainable = trainable
x.add(layer)
# Transfer learning, load previous weights
x.load_weights(r'C:\temp\vgg19_weights_tf_dim_ordering_tf_kernels.h5')
# Remove last layer
x.pop()
# Add new dense layer
#x.add(layers.Dropout(0.1))
x.add(layers.Dense(8, activation='sigmoid'))
# optimizer = tensorflow.keras.optimizers.SGD(learning_rate=1e-3)
sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=False)
print('Configuration Start -------------------------')
print(sgd.get_config())
print('Configuration End -------------------------')
x.compile(optimizer=sgd, loss='binary_crossentropy', metrics=self.metrics)
self.show_summary(x)
self.plot_summary(x, 'model_vgg19net.png')
return x