Primary version
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.layers import Dense, GlobalAveragePooling2D, UpSampling2D, Permute, Reshape, Softmax, Add
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_160.h5'
INPUT_SHAPE = (160, 160, 3)
base_model = tf.keras.applications.MobileNetV2(
input_shape = INPUT_SHAPE, alpha=1,
weights = WEIGHTS_PATH
)
last_layer = base_model.layers[-2].output
dense_layer = Dense(classes)
output_pred = Softmax(name="prediction")(dense_layer(last_layer))
conv_layer = base_model.layers[-4].output
reshape_layer = Reshape((conv_layer.shape[1] * conv_layer.shape[2] , -1))(conv_layer)
dot_output = dense_layer(reshape_layer)
transpose = Permute((2, 1))(dot_output)
reshape_2_layer = Reshape((-1, conv_layer.shape[1] , conv_layer.shape[2]))(transpose)
SIZE = (int(INPUT_SHAPE[1] / conv_layer.shape[2]),
int(INPUT_SHAPE[0] / conv_layer.shape[1]))
output_act_map = UpSampling2D(size=SIZE, interpolation="bilinear", data_format="channels_first", name="activation_map")(reshape_2_layer)
model = Model(inputs=base_model.inputs, outputs=[output_pred, output_act_map])
TRAINABLE_START_IDX = -12
for layer in model.layers[:TRAINABLE_START_IDX]:
layer.trainable = False
print(model.summary())
print("Debug:", model.layers[TRAINABLE_START_IDX].name, model.layers[TRAINABLE_START_IDX].trainable)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),
loss={'prediction': 'categorical_crossentropy', 'activation_map': None
},
metrics={'prediction': ['accuracy'], 'activation_map': [None]
})
BATCH_SIZE = 32
EPOCHS=30
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, epochs=EPOCHS))
model.fit(train_dataset, validation_data=validation_dataset, epochs=EPOCHS, verbose=2, callbacks=callbacks)
print('')
print('Training done.', flush=True)
Input layer (76,800 features)
MobileNetV2 96x96 0.35 (final layer: 16 neurons, 0.1 dropout)
Output layer (3 classes)
Model
Model version:
