def main(config):
# Load CIFAR data
data = DataLoader(config)
train_loader, test_loader = data.prepare_data()
model = DenseNet(config)
model.build((config["trainer"]["batch_size"], 224, 224, 3))
print(model.summary())
optimizer = tf.keras.optimizers.Adam(lr=0.001)
loss_object = tf.keras.losses.CategoricalCrossentropy()
train_loss = tf.keras.metrics.Mean(name="loss", dtype=tf.float32)
train_accuracy = tf.keras.metrics.CategoricalAccuracy(
name='train_accuracy')
def train_step(images, labels):
with tf.GradientTape() as tape:
predictions = model(images)
loss = loss_object(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_loss(loss)
train_accuracy(labels, predictions)
for epoch in range(config["trainer"]["epochs"]):
for step, (images, labels) in tqdm(
enumerate(train_loader),
total=int(len(data) / config["trainer"]["batch_size"])):
train_step(images, labels)
template = 'Epoch {}, Loss: {:.4f}, Accuracy: {:.4f}'
print(
template.format(epoch + 1, train_loss.result(),
train_accuracy.result() * 100))
if 'model-last_epoch.h5' in os.listdir('outputs/'):
print ('last model loaded')
model= load_model('outputs/model-last_epoch.h5')
else:
print('created a new model instead')
model = DenseNet(input_shape= (r,c,1), dense_blocks=5, dense_layers=-1, growth_rate=8, dropout_rate=0.2,
bottleneck=True, compression=1.0, weight_decay=1e-4, depth=40)
# training parameters
adamOpt = Adam(lr=0.0001)
model.compile(loss='mean_squared_error', optimizer=adamOpt, metrics=['mae', 'mse'])
model.summary(line_length=200)
model.save("test")
# log_filename = 'outputs/' + 'landmarks' +'_results.csv'
#
# csv_log = callbacks.CSVLogger(log_filename, separator=',', append=True)
#
#
#
# checkpoint = callbacks.ModelCheckpoint(checkpoint_filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
#
# callbacks_list = [csv_log, checkpoint]
# callbacks_list.append(ReduceLROnPlateau(factor=reduceLearningRate, patience=200,
# verbose=True))
# callbacks_list.append(EarlyStopping(verbose=True, patience=200))
#