def model1(model_tag, size, activation, weight):
IMG_HEIGHT, IMG_WIDTH, NUM_CHANNELS, OUTPUT_CHANNELS = size
inputs = tf.keras.layers.Input((IMG_HEIGHT, IMG_WIDTH, NUM_CHANNELS))
block1_out, block1_skip_out = inception_block(inputs, 16, (3, 3), (2, 2), activation, 'down1')
block2_out, block2_skip_out = inception_block(block1_out, 32, (3, 3), (2, 2), activation, 'down2')
block3_out, block3_skip_out = inception_block(block2_out, 64, (3, 3), (2, 2), activation, 'down3')
block4_out, block4_skip_out = inception_block(block3_out, 128, (3, 3), (2, 2), activation, 'down4')
block5_out, block5_skip_out = inception_block(block4_out, 256, (3, 3), (2, 2), activation, 'down5')
### connector
block6a = tf.keras.layers.Conv2D(512, (3, 3), activation = activation, kernel_initializer = 'he_normal', padding = 'same')(block5_out)
block6b = tf.keras.layers.Conv2D(512, (3, 3), activation = activation, kernel_initializer = 'he_normal', padding = 'same')(block6a)
### up 1
block7_out = upsampling_block(block6b, block5_skip_out, 256, (3, 3), (2, 2), activation, 'up1')
block8_out = upsampling_block(block7_out, block4_skip_out, 128, (3, 3), (2, 2), activation, 'up2')
block9_out = upsampling_block(block8_out, block3_skip_out, 64, (3, 3), (2, 2), activation, 'up3')
block10_out = upsampling_block(block9_out, block2_skip_out, 32, (3, 3), (2, 2), activation, 'up4')
block11_out = upsampling_block(block10_out, block1_skip_out, 16, (3, 3), (2, 2), activation, 'up5')
outputs = tf.keras.layers.Conv2D(1, (1, 1))(block11_out)
model = tf.keras.Model(inputs = [inputs], outputs = [outputs])
optimizer = tf.keras.optimizers.Adam(learning_rate = 0.0001)
#model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
model.compile(optimizer = optimizer, loss = weighted_binary_crossentropy(pos_weight=weight), metrics = ['accuracy'])
model.summary()
return model
def model1():
data_dir = base_dir + '/ultrasound-nerve-segmentation/train_data_sample'
model_dir = base_dir + '/models'
IMG_HEIGHT = 128
IMG_WIDTH = 128
NUM_CHANNELS = 3
OUTPUT_CHANNELS = 1
train_data = load_data(data_dir, [IMG_WIDTH, IMG_HEIGHT])
print(train_data)
#N = 5635
N = 500
tag = 'unet_model1_rrelu_dice'
model_dir += '/{0}'.format(tag)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
# for image, mask in train_data.take(1):
# sample_image, sample_mask = image, mask
### down 1
inputs = tf.keras.layers.Input((IMG_HEIGHT, IMG_WIDTH, NUM_CHANNELS))
pooling1, conv1b = downsampling_block(inputs, 16, (3, 3), (2, 2), 'relu',
'down1')
### down 2
pooling2, conv2b = downsampling_block(pooling1, 32, (3, 3), (2, 2), 'relu',
'down2')
### down 3
pooling3, conv3b = downsampling_block(pooling2, 64, (3, 3), (2, 2), 'relu',
'down3')
### down 4
pooling4, conv4b = downsampling_block(pooling3, 128, (3, 3), (2, 2),
'relu', 'down4')
### down 5
conv5a = tf.keras.layers.Conv2D(256, (3, 3),
activation=tf.keras.activations.relu,
kernel_initializer='he_normal',
padding='same')(pooling4)
conv5b = tf.keras.layers.Conv2D(256, (3, 3),
activation=tf.keras.activations.relu,
kernel_initializer='he_normal',
padding='same')(conv5a)
### up 1
conv6b = upsampling_block(conv5b, conv4b, 128, (3, 3), (2, 2), 'relu',
'up1')
### up 2
conv7b = upsampling_block(conv6b, conv3b, 64, (3, 3), (2, 2), 'relu',
'up2')
### up 3
conv8b = upsampling_block(conv7b, conv2b, 32, (3, 3), (2, 2), 'relu',
'up3')
### up 4
conv9b = upsampling_block(conv8b, conv1b, 16, (3, 3), (2, 2), 'relu',
'up4')
outputs = tf.keras.layers.Conv2D(1, (1, 1))(conv9b)
#outputs = tf.keras.layers.Conv2D(1, (1, 1), activation = 'sigmoid')(conv9b)
model_save_callback = tf.keras.callbacks.ModelCheckpoint(
model_dir + '/model_epoch_{epoch:04d}.h5',
save_weights_only=True,
period=25)
#model = tf.keras.Model(inputs = [inputs], outputs = [outputs])
model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
#model.compile(optimizer = 'adam', loss = 'binary_crossentropy', from_logits = True, metrics = ['accuracy'])
#model.compile(optimizer = 'adam', loss = weighted_binary_crossentropy(pos_weight=10), from_logits = True, metrics = ['accuracy'])
#model.compile(optimizer = 'adam', loss = weighted_binary_crossentropy(pos_weight=10), metrics = ['accuracy'])
model.compile(optimizer='adam',
loss=dice_coefficient(),
metrics=['accuracy'])
#model.compile(optimizer = 'adam', loss = tversky_loss(0.1), metrics = ['accuracy'])
model.summary()
#sys.exit(0)
# model.fit(x_train, y_train, validation_split=0.1, batch_size=16, epochs=20,
# callbacks=callbacks)
BATCH_SIZE = 32
BUFFER_SIZE = 1000
STEPS_PER_EPOCH = N // BATCH_SIZE
train_dataset = train_data.shuffle(BUFFER_SIZE).batch(BATCH_SIZE).repeat()
train_dataset = train_dataset.prefetch(
buffer_size=tf.data.experimental.AUTOTUNE)
#test_dataset = test.batch(BATCH_SIZE)
EPOCHS = 250
VAL_SUBSPLITS = 5
#VALIDATION_STEPS = info.splits['test'].num_examples//BATCH_SIZE//VAL_SUBSPLITS
VALIDATION_STEPS = N // BATCH_SIZE // VAL_SUBSPLITS
model_history = model.fit(train_dataset,
epochs=EPOCHS,
steps_per_epoch=STEPS_PER_EPOCH,
validation_steps=VALIDATION_STEPS,
validation_data=train_dataset,
callbacks=[model_save_callback])
sigmoid = lambda x: 1 / (1 + np.exp(-1 * x))
model.save(model_dir + '/model_final.h5')
