Tensorflow model for estimating angles from images based on tf.estimator.Estimators.

Each model is identified by a model_name. Parameters are defined in params/model_name.json. Each model follows a typical convolution > dense layer chain, with optional dropout and batch normalization. The output for each example is either a probability distribution representing confidences of a given angle, or an angle in the range (-pi, pi].

• estimator.py: provides the ModelBuilder class for building various parts of the graph, along with get_estimator and get_model_dir for use with tf.estimator.Estimator
• data.py: provides data io functions.
• server.py: example of how to use for custom predictions. Note this doesn't use tensorflow's TfServing, so should be treated as an example only.
• train.py: training script.
• losses.py: provides losses used.

1. Change the load_data function in data.py to something meaningful. Current implementation is random input for debugging purposes.
2. Define a model by creating a new .json file in params (or use base)
3. Train: python train.py my_model_name -b 64 -s 10000000 trains with batch size of 64 and maximum number of steps 10000000.

(conv (-> batch_norm)? -> tf.nn.relu (-> dropout)?)* -> flatten -> (dense (-> batch_norm)? -> tf.nn.relu (-> dropout)?)* (-> atan)?

See params/base.json for an example parameterization of the models.

• conv_kernels: list of ints, e.g. [3, 3, 3]. Kernel size for each convolutional layer
• conv_filters: list of ints, e.g. [32, 64, 64]. Number of filters for each convolutional layer
• padding: one of ['VALID', 'SAME']. Used in tf.layers.conv2d and tf.layers.max_pool2d.
• dense_nodes: list of ints, e.g. [1024, 128]. Number dense nodes after convolutional layers and before the final fully connected layer.
• target: one of ['angle', 'distribution']. Indicates whether the output of the nextwork should be an angle (in which case the network infers an (x, y) value and calculates the angle via tf.atan2) or a distribution (see n_bins).
• n_bins: int, only applicable if target == 'distribution'. Number of bins to divide the range (-pi, pi] into.
• dropout_rate: float in [0, 1). Dropout rate used after each convolution/dense layer. Ignored if 0.
• use_batch_norm: bool indicating whether or not to use batch_norm. If true, it is applied before the activation (relu) and dropout.

• Use cosine loss
• Move dropout to before batch_norm
• Different activation function (from tf.nn.relu)
• More convolutions between each max_pool2d
• Average pooling, or strided convolutional layers.