.
It's not our burden.
It's not our struggle.
I talk a lot about these issues.
of whether we deserve to kill.
I tell them about slavery.
that went on to World War II.
We don't really know very much about it.
that was an era defined by terror.
In many communities, people had to worry about being lynched.
They had to worry about being bombed.
It was the threat of terror that shaped their lives.
They tell me to say, "No, tell them that we grew up with that."
where we really don't like to talk about our problems.
We don't like to talk about our history.
what it's meant to do the things we've done historically.
We're constantly running into each other.
We're constantly creating tensions and conflicts.Q: Error: Value rank (4) should be larger than the Variable rank (1) at most by number of dynamic axes (2) I would like to know How to provide input varibales to CNTK Network Modelling and training the model like given here
Since i  have two independent variable and 3 target variables, X and Y are of shape:  
X["train"].shape
Out[60]: (34567, 10, 2, 1)

Y["train"].shape
Out[61]: (34567, 3, 1)

i gave time_steps and time_shift as 10. 
N = 10  ### Specify the internal-state dimensions of the LSTM cell which is 10
def create_model(x):
    with C.layers.default_options(initial_state = 0.1):
        m = C.layers.Recurrence(C.layers.LSTM(N))(x)
        m = C.sequence.last(m)
        m = C.layers.Dropout(0.2, seed=1)(m)
        m = C.layers.Dense(1)(m)
        return m


BATCH_SIZE = 100;EPOCHS = 100

def next_batch(x, y, ds):
    def as_batch(data, start, count):
        part = []
        for i in range(start, start + count):
            part.append(data[i])
        return np.array(part)
    for i in range(0, len(x[ds])-BATCH_SIZE, BATCH_SIZE):
        yield as_batch(x[ds], i, BATCH_SIZE), as_batch(y[ds], i, BATCH_SIZE)



x = C.sequence.input_variable(1)
z = create_model(x)
l = C.input_variable(1, dynamic_axes=z.dynamic_axes, name="y")
learning_rate = 0.02
lr_schedule = C.learning_parameter_schedule(learning_rate)
loss = C.squared_error(z, l)
error = C.squared_error(z, l)
momentum_schedule = C.momentum_schedule(0.9, minibatch_size=BATCH_SIZE)
learner = C.fsadagrad(z.parameters, 
                      lr = lr_schedule, 
                      momentum = momentum_schedule, 
                      unit_gain = True)
trainer = C.Trainer(z, (loss, error), [learner])



# train
loss_summary = []
for epoch in range(0, EPOCHS):
    for x1, y1 in next_batch(X, Y, "train"):
        trainer.train_minibatch({x: x1, l: y1})
    if epoch % (EPOCHS / 10) == 0:
        training_loss = trainer.previous_minibatch_loss_average
        loss_summary.append(training_loss)
        print("epoch: {}, loss: {:.5f}".format(epoch, training_loss))

But this produces error : 

Traceback (most recent call last):
File "", line 6, in 
      trainer.train_minibatch({x: x1, l: y1})
File "c:\program
  files\python36\lib\site-packages\cntk\train\trainer.py", line 184, in
  train_minibatch
      device)
File "c:\program files\python36\lib\site-packages\cntk\cntk_py.py",
  line 2856, in train_minibatch
      return _cntk_py.Trainer_train_minibatch(self, *args)
ValueError: Value rank (4) should be larger than the Variable rank (1)
  at most by number of dynamic axes (2); Variable = 'Input('Input4973',
  [#, *], 1)', Value shape = '[100 x 10 x 2 x 1]'.
[CALL STACK]

CNTK::TrainingParameterSchedule::  Transform
        - CNTK::Internal::  UseSparseGradientAggregationInDataParallelSGD (x2)
        - CNTK::Function::  Forward
        - CNTK::  CreateTrainer
        - CNTK::Trainer::  TotalNumberOfUnitsSeen
        - CNTK::Trainer::  TrainMinibatch (x2)
        - PyInit__cntk_py (x2)
        - PyEval_EvalFrameDefault
        - Py_CheckFunctionResult
        - PyObject_CallFunctionObjArgs
        - PyEval_EvalFrameDefault
        - Py_CheckFunctionResult
        - PyObject_CallFunctionObjArgs


I know this error corresponds to dynamic axes and input variables. But i don't know what to give as input variable in C.sequence.input_variable(1)  and  C.input_variable(1, dynamic_axes=z.dynamic_axes, name="y")
Also, i want to predict three target variables 

A: To predict 3 target variables you will want to have 3 outputs in your last layer so you should change m = C.layers.Dense(1)(m) to m = C.layers.Dense(3)(m)
The other error is because your X["train"] has shape=(batch, sequence, 2, 1), but when you call create_model you pass something that has shape=(sequence, 1). The best way to fix this is make your X["train"] have shape=(batch, sequence, 2) (e.g. by calling np.squeeze) and then call create_model with C.sequence.input_variable(2).
The new 4-inch iPhone would be available online and in retail stores on March 18. Apple is unlikely to offer pre-orders for the new devices, tech website 9to5mac reported on Friday.
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