[PYTHON] How to cast with Theano

Cast in Theano

theano is a very strict typed library.

There is no powerful type inference like python, so of course you will want to take advantage of the cast.

I will cast it for the time being

The cast of theano is implemented by theano.tensor.cast ().

Let's use cast immediately. By default, a matrix defined in float64 type is converted to int32 type.

python

import theano.tensor as T
x = T.matrix()
x_as_int = T.cast(x, 'int32')

type(x), type(x_as_int)

Output result

(theano.tensor.var.TensorVariable, theano.tensor.var.TensorVariable)

Isn't it cast?

In the above results, both show the same type (TensorVariable).

This is because Theano's specification defines symbols as variables, not values.

Use theano.printing.debugprint () to see the type of the symbol's contents.

type of x

import theano
theano.printing.debugprint(x)

Output result

<TensorType(float64, matrix)> [id A]

x_as_int type

theano.printing.debugprint(x_as_int)

Output result

Elemwise{Cast{int32}} [id A] ''   
 |<TensorType(float64, matrix)> [id B]

I see, it was originally a float64 type, but in x_as_int it seems to be cast to int32.

Let's actually enter the value and check the behavior.

Check type through function

To put a value in a symbol, you need to define a function.

By defining a formula using symbols as a function and entering a value in that function, the processing result of the formula is returned as the return value of the function.

At this time, the type of the input value depends on the symbol type.

If you put in an inappropriate type, you will get an error, so

If you set the int32 symbol x_as_int as input, you should get an error if you enter a decimal.

Function definition

import numpy as np

mat = np.array([[1.0, 0.0], [0.0, 1.0]], dtype="float64")
mat_int = np.array([[1, 0], [0, 1]], dtype="int32")

y = x * 2
f = theano.function(inputs=[x], outputs=y)

y_as_int = x_as_int * 2
f_as_int = theano.function(inputs=[x_as_int], outputs=y_as_int)

f(x)

f(mat)

Execution result

array([[ 2.,  0.],
       [ 0.,  2.]])

f(x_as_int)

f(mat_int)

Execution result

array([[ 2.,  0.],
       [ 0.,  2.]])

f_as_int(x)

f_as_int(mat)

Execution result

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-56-31692f0163e9> in <module>()
----> 1 f_as_int(mat)

/home/ubuntu/anaconda3/lib/python3.5/site-packages/theano/compile/function_module.py in __call__(self, *args, **kwargs)
    786                         s.storage[0] = s.type.filter(
    787                             arg, strict=s.strict,
--> 788                             allow_downcast=s.allow_downcast)
    789 
    790                     except Exception as e:

/home/ubuntu/anaconda3/lib/python3.5/site-packages/theano/tensor/type.py in filter(self, data, strict, allow_downcast)
    138                             '"function".'
    139                             % (self, data.dtype, self.dtype))
--> 140                         raise TypeError(err_msg, data)
    141                 elif (allow_downcast is None and
    142                         type(data) is float and

TypeError: ('Bad input argument to theano function with name "<ipython-input-54-50af382d0dd4>:2" at index 0 (0-based)', 'TensorType(int32, matrix) cannot store a value of dtype float64 without risking loss of precision. If you do not mind this loss, you can: 1) explicitly cast your data to int32, or 2) set "allow_input_downcast=True" when calling "function".', array([[ 1.,  0.],
       [ 0.,  1.]]))

f_as_int(x_as_int)

array([[2, 0],
       [0, 2]], dtype=int32)

As expected, f_as_int (mat) with the input value set to float64 type while specifying the int32 type as the input symbol threw an error.

Now you can see the cast movement in theano safely.