Training settings
Please provide a valid training processor option
Neural network architecture
import math
from pathlib import Path
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, InputLayer, Dropout, Conv1D, Flatten, Reshape, MaxPooling1D, BatchNormalization, Conv2D, GlobalMaxPooling2D, Lambda
from tensorflow.keras.optimizers import Adam, Adadelta
from tensorflow.keras.losses import categorical_crossentropy
sys.path.append('./resources/libraries')
import ei_tensorflow.training
WEIGHTS_PATH = './transfer-learning-weights/keras/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224.h5'
INPUT_SHAPE = (224, 224, 3)
base_model = tf.keras.applications.MobileNetV2(
input_shape = INPUT_SHAPE, alpha=1,
weights = WEIGHTS_PATH
)
base_model.trainable = False
model = Sequential()
model.add(InputLayer(input_shape=INPUT_SHAPE, name='x_input'))
# Don't include the base model's top layers
last_layer_index = -3
model.add(Model(inputs=base_model.inputs, outputs=base_model.layers[last_layer_index].output))
model.add(Reshape((-1, model.layers[-1].output.shape[3])))
model.add(Dropout(0.1))
model.add(Flatten())
model.add(Dense(classes, activation='softmax'))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),
loss='categorical_crossentropy',
metrics=['accuracy'])
# Implements the data augmentation policy
def augment_image(image, label):
# Flips the image randomly
image = tf.image.random_flip_left_right(image)
# Increase the image size, then randomly crop it down to
# the original dimensions
resize_factor = random.uniform(1, 1.2)
new_height = math.floor(resize_factor * INPUT_SHAPE[0])
new_width = math.floor(resize_factor * INPUT_SHAPE[1])
image = tf.image.resize_with_crop_or_pad(image, new_height, new_width)
image = tf.image.random_crop(image, size=INPUT_SHAPE)
# Vary the brightness of the image
image = tf.image.random_brightness(image, max_delta=0.2)
return image, label
train_dataset = train_dataset.map(augment_image, num_parallel_calls=tf.data.AUTOTUNE)
BATCH_SIZE = 32
train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=False)
validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False)
callbacks.append(BatchLoggerCallback(BATCH_SIZE, train_sample_count))
model.fit(train_dataset, validation_data=validation_dataset, epochs=20, verbose=2, callbacks=callbacks)
print('')
print('Initial training done.', flush=True)
# How many epochs we will fine tune the model
FINE_TUNE_EPOCHS = 10
# What percentage of the base model's layers we will fine tune
FINE_TUNE_PERCENTAGE = 65
print('Fine-tuning best model for {} epochs...'.format(FINE_TUNE_EPOCHS), flush=True)
# Load best model from initial training
model = ei_tensorflow.training.load_best_model(BEST_MODEL_PATH)
# Determine which layer to begin fine tuning at
model_layer_count = len(model.layers)
fine_tune_from = math.ceil(model_layer_count * ((100 - FINE_TUNE_PERCENTAGE) / 100))
# Allow the entire base model to be trained
model.trainable = True
# Freeze all the layers before the 'fine_tune_from' layer
for layer in model.layers[:fine_tune_from]:
layer.trainable = False
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.000045),
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_dataset,
epochs=FINE_TUNE_EPOCHS,
verbose=2,
validation_data=validation_dataset,
callbacks=callbacks,
class_weight=None
)
Input layer (150,528 features)
MobileNetV2 160x160 1.0 (no final dense layer, 0.1 dropout)
Output layer (7 classes)
Model
Model version: