What is this value coming from

[rafikg]

keras_Realtime_Multi-Person_Pose_Estimation/training/train_pose.py

Line 27 in b595cfb

stepsize = 136106 #68053 // after each stepsize iterations update learning rate: lr=lr*gamma

?

[michalfaber]

Yes, indeed it looks strange. It's been taken from the original caffe version of this repo. In caffe it is a parameter (number of iterations) of a solver. I don't plan to refactor this code as I am working on a new version for Tensorflow 2.0

[rafikg]

In the original paper, they did not say anything about the training scheme (optimizer, learning rate, epochs,...).
I reimplement your model with tf.keras and everything was good except the MultiSGD where I get an error here is my code:

@tf_export('keras.optimizers.MultiSGD')
class MultiSGD(Optimizer):
    """Stochastic gradient descent optimizer.

    Includes support for momentum,
    learning rate decay, and Nesterov momentum.

    Arguments:
        lr: float >= 0. Learning rate.
        momentum: float >= 0. Parameter that accelerates SGD
            in the relevant direction and dampens oscillations.
        decay: float >= 0. Learning rate decay over each update.
        nesterov: boolean. Whether to apply Nesterov momentum.
    """

    def __init__(self, lr=0.01, momentum=0., decay=0., nesterov=False, lr_mult=None, **kwargs):
        super(MultiSGD, self).__init__(**kwargs)
        with K.name_scope(self.__class__.__name__):
            self.iterations = K.variable(0, dtype='int64', name='iterations')
            self.lr = K.variable(lr, name='lr')
            self.momentum = K.variable(momentum, name='momentum')
            self.decay = K.variable(decay, name='decay')
        self.initial_decay = decay
        self.nesterov = nesterov
        self.lr_mult = lr_mult

    def get_updates(self, loss, params):
        grads = self.get_gradients(loss, params)
        self.updates = [state_ops.assign_add(self.iterations, 1)]

        lr = self.lr
        if self.initial_decay > 0:
            lr = lr * (  # pylint: disable=g-no-augmented-assignment
                    1. / (1. + self.decay * math_ops.cast(self.iterations,
                                                          K.dtype(self.decay))))
        # momentum
        shapes = [K.int_shape(p) for p in params]
        moments = [K.zeros(shape) for shape in shapes]
        self.weights = [self.iterations] + moments
        for p, g, m in zip(params, grads, moments):
            if p.name in self.lr_mult:
                multiplied_lr = lr * self.lr_mult[p.name]
            else:
                multiplied_lr = lr
            # v = self.momentum * m - lr * g  # velocity
            v = self.momentum * m - multiplied_lr * g  # velocity
            self.updates.append(state_ops.assign(m, v))

            if self.nesterov:
                # new_p = p + self.momentum * v - lr * g
                new_p = p + self.momentum * v - multiplied_lr * g
            else:
                new_p = p + v

            # Apply constraints.
            if getattr(p, 'constraint', None) is not None:
                new_p = p.constraint(new_p)

            self.updates.append(state_ops.assign(p, new_p))
        return self.updates

    def get_config(self):
        config = {
            'lr': float(K.get_value(self.lr)),
            'momentum': float(K.get_value(self.momentum)),
            'decay': float(K.get_value(self.decay)),
            'nesterov': self.nesterov
        }
        base_config = super(MultiSGD, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

tensorflow.python.framework.errors_impl.FailedPreconditionError: Error while reading resource variable training/MultiSGD/Variable_180 from Container: localhost. This could mean that the variable was uninitialized. Not found: Resource localhost/training/MultiSGD/Variable_180/N10tensorflow3VarE does not exist.
[[{{node training/MultiSGD/mul_541/ReadVariableOp}} = ReadVariableOpdtype=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"]]
[[{{node metrics/acc_9/Mean/_791}} = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_8739_metrics/acc_9/Mean", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]