@matkelcey I have shared a FOMO lines model with you on the expert network ID=109651 called rocksetta-lines-fomo-0stop-1right-2left-3straight That link might work if you are logged in.

Presently I have trained it only on the FOMO lines and here is the code that seems relevant:

```
cut_point = mobile_net_v2.get_layer('block_6_expand_relu')
#! Now attach a small additional head on the MobileNet
model = Conv2D(filters=32, kernel_size=1, strides=1,
activation='relu', name='head')(cut_point.output)
logits = Conv2D(filters=num_classes, kernel_size=1, strides=1,
activation=None, name='logits')(model)
return Model(inputs=mobile_net_v2.input, outputs=logits)
```

I am in no rush to do this but would like to understand what you are thinking I should try.

- train model using only “lines” - done
- freeze all present layers
- add a few more layers as mentioned above
- retrain model with “lines” but now with the specific bounding box labels: (ostop, 1right, 2left, 3straight)
- Use the output to control my toy car.

And here is the entire model keras expert mode.

```
sys.path.append('./resources/libraries')
import os
import tensorflow as tf
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import BatchNormalization, Conv2D
from tensorflow.keras.models import Model
from ei_tensorflow.constrained_object_detection import models, dataset, metrics, util
import ei_tensorflow.training
def build_model(input_shape: tuple, weights: str, alpha: float,
num_classes: int) -> tf.keras.Model:
""" Construct a constrained object detection model.
Args:
input_shape: Passed to MobileNet construction.
weights: Weights for initialization of MobileNet where None implies
random initialization.
alpha: MobileNet alpha value.
num_classes: Number of classes, i.e. final dimension size, in output.
Returns:
Uncompiled keras model.
Model takes (B, H, W, C) input and
returns (B, H//8, W//8, num_classes) logits.
"""
#! First create full mobile_net_V2 from (HW, HW, C) input
#! to (HW/8, HW/8, C) output
mobile_net_v2 = MobileNetV2(input_shape=input_shape,
weights=weights,
alpha=alpha,
include_top=True)
#! Default batch norm is configured for huge networks, let's speed it up
for layer in mobile_net_v2.layers:
if type(layer) == BatchNormalization:
layer.momentum = 0.9
#! Cut MobileNet where it hits 1/8th input resolution; i.e. (HW/8, HW/8, C)
cut_point = mobile_net_v2.get_layer('block_6_expand_relu')
#! Now attach a small additional head on the MobileNet
model = Conv2D(filters=32, kernel_size=1, strides=1,
activation='relu', name='head')(cut_point.output)
logits = Conv2D(filters=num_classes, kernel_size=1, strides=1,
activation=None, name='logits')(model)
return Model(inputs=mobile_net_v2.input, outputs=logits)
def train(num_classes: int, learning_rate: float, num_epochs: int,
alpha: float, object_weight: int,
train_dataset: tf.data.Dataset,
validation_dataset: tf.data.Dataset,
best_model_path: str,
input_shape: tuple) -> tf.keras.Model:
""" Construct and train a constrained object detection model.
Args:
num_classes: Number of classes in datasets. This does not include
implied background class introduced by segmentation map dataset
conversion.
learning_rate: Learning rate for Adam.
num_epochs: Number of epochs passed to model.fit
alpha: Alpha used to construct MobileNet. Pretrained weights will be
used if there is a matching set.
object_weight: The weighting to give the object in the loss function
where background has an implied weight of 1.0.
train_dataset: Training dataset of (x, (bbox, one_hot_y))
validation_dataset: Validation dataset of (x, (bbox, one_hot_y))
best_model_path: location to save best model path. note: weights
will be restored from this path based on best val_f1 score.
input_shape: The shape of the model's input
max_training_time_s: Max training time (will exit if est. training time is over the limit)
is_enterprise_project: Determines what message we print if training time exceeds
Returns:
Trained keras model.
Constructs a new constrained object detection model with num_classes+1
outputs (denoting the classes with an implied background class of 0).
Both training and validation datasets are adapted from
(x, (bbox, one_hot_y)) to (x, segmentation_map). Model is trained with a
custom weighted cross entropy function.
"""
nonlocal callbacks
num_classes_with_background = num_classes + 1
input_width_height = None
width, height, input_num_channels = input_shape
if width != height:
raise Exception(f"Only square inputs are supported; not {input_shape}")
input_width_height = width
#! Use pretrained weights, if we have them for configured
weights = None
if input_num_channels == 1:
if alpha == 0.1:
weights = "./transfer-learning-weights/edgeimpulse/MobileNetV2.0_1.96x96.grayscale.bsize_64.lr_0_05.epoch_441.val_loss_4.13.val_accuracy_0.2.hdf5"
elif alpha == 0.35:
weights = "./transfer-learning-weights/edgeimpulse/MobileNetV2.0_35.96x96.grayscale.bsize_64.lr_0_005.epoch_260.val_loss_3.10.val_accuracy_0.35.hdf5"
elif input_num_channels == 3:
if alpha == 0.1:
weights = "./transfer-learning-weights/edgeimpulse/MobileNetV2.0_1.96x96.color.bsize_64.lr_0_05.epoch_498.val_loss_3.85.hdf5"
elif alpha == 0.35:
weights = "./transfer-learning-weights/keras/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.35_96.h5"
if (weights is not None) and (not os.path.exists(weights)):
print(f"WARNING: Pretrained weights {weights} unavailable; defaulting to random init")
weights = None
model = build_model(
input_shape=input_shape,
weights=weights,
alpha=alpha,
num_classes=num_classes_with_background
)
#! Derive output size from model
model_output_shape = model.layers[-1].output.shape
_batch, width, height, num_classes = model_output_shape
if width != height:
raise Exception(f"Only square outputs are supported; not {model_output_shape}")
output_width_height = width
#! Build weighted cross entropy loss specific to this model size
weighted_xent = models.construct_weighted_xent_fn(model.output.shape, object_weight)
model.compile(loss=weighted_xent,
optimizer=Adam(learning_rate=learning_rate))
#! Wrap bbox datasets with adapters for segmentation maps
train_segmentation_dataset = dataset.bbox_to_segmentation(
train_dataset, input_width_height, input_num_channels,
output_width_height, num_classes_with_background)
validation_segmentation_dataset = dataset.bbox_to_segmentation(
validation_dataset, input_width_height, input_num_channels,
output_width_height, num_classes_with_background)
#! Initialise bias of final classifier based on training data prior.
util.set_classifier_biases_from_dataset(
model, train_segmentation_dataset, num_classes_with_background)
#! Create callback that will do centroid scoring on end of epoch against
#! validation data. Include a callback to show % progress in slow cases.
callbacks = callbacks if callbacks else []
callbacks.append(metrics.CentroidScoring(validation_dataset, output_width_height, num_classes_with_background))
callbacks.append(metrics.PrintPercentageTrained(num_epochs))
#! Include a callback for model checkpointing based on the best validation f1.
callbacks.append(
tf.keras.callbacks.ModelCheckpoint(best_model_path,
monitor='val_f1', save_best_only=True, mode='max',
save_weights_only=True, verbose=0))
model.fit(train_segmentation_dataset,
validation_data=validation_segmentation_dataset,
epochs=num_epochs, callbacks=callbacks, verbose=0)
#! Restore best weights.
model.load_weights(best_model_path)
return model
model = train(num_classes=classes,
learning_rate=0.001,
num_epochs=700,
alpha=0.35,
object_weight=100,
train_dataset=train_dataset,
validation_dataset=validation_dataset,
best_model_path=BEST_MODEL_PATH,
input_shape=MODEL_INPUT_SHAPE)
override_mode = 'segmentation'
disable_per_channel_quantization = False
```