Esempi in Python per tensor_conv2d

Esempio n. 1

def f(x):

    var000 = tf.constant(
        numpy.asarray(
            json.loads(
                '[[[[0.02112241425389188, 0.0, 0.0], [0.0, 0.02112241425389188, 0.0], [0.0, 0.0, 0.02112241425389188]], [[0.03206888656210742, 0.0, 0.0], [0.0, 0.03206888656210742, 0.0], [0.0, 0.0, 0.03206888656210742]], [[0.038952921396240715, 0.0, 0.0], [0.0, 0.038952921396240715, 0.0], [0.0, 0.0, 0.038952921396240715]], [[0.03206888656210742, 0.0, 0.0], [0.0, 0.03206888656210742, 0.0], [0.0, 0.0, 0.03206888656210742]], [[0.02112241425389188, 0.0, 0.0], [0.0, 0.02112241425389188, 0.0], [0.0, 0.0, 0.02112241425389188]]], [[[0.032068886562107414, 0.0, 0.0], [0.0, 0.032068886562107414, 0.0], [0.0, 0.0, 0.032068886562107414]], [[0.04868825471235253, 0.0, 0.0], [0.0, 0.04868825471235253, 0.0], [0.0, 0.0, 0.04868825471235253]], [[0.05913986926416638, 0.0, 0.0], [0.0, 0.05913986926416638, 0.0], [0.0, 0.0, 0.05913986926416638]], [[0.04868825471235253, 0.0, 0.0], [0.0, 0.04868825471235253, 0.0], [0.0, 0.0, 0.04868825471235253]], [[0.032068886562107414, 0.0, 0.0], [0.0, 0.032068886562107414, 0.0], [0.0, 0.0, 0.032068886562107414]]], [[[0.038952921396240715, 0.0, 0.0], [0.0, 0.038952921396240715, 0.0], [0.0, 0.0, 0.038952921396240715]], [[0.05913986926416638, 0.0, 0.0], [0.0, 0.05913986926416638, 0.0], [0.0, 0.0, 0.05913986926416638]], [[0.07183506899653452, 0.0, 0.0], [0.0, 0.07183506899653452, 0.0], [0.0, 0.0, 0.07183506899653452]], [[0.05913986926416638, 0.0, 0.0], [0.0, 0.05913986926416638, 0.0], [0.0, 0.0, 0.05913986926416638]], [[0.038952921396240715, 0.0, 0.0], [0.0, 0.038952921396240715, 0.0], [0.0, 0.0, 0.038952921396240715]]], [[[0.032068886562107414, 0.0, 0.0], [0.0, 0.032068886562107414, 0.0], [0.0, 0.0, 0.032068886562107414]], [[0.04868825471235253, 0.0, 0.0], [0.0, 0.04868825471235253, 0.0], [0.0, 0.0, 0.04868825471235253]], [[0.05913986926416638, 0.0, 0.0], [0.0, 0.05913986926416638, 0.0], [0.0, 0.0, 0.05913986926416638]], [[0.04868825471235253, 0.0, 0.0], [0.0, 0.04868825471235253, 0.0], [0.0, 0.0, 0.04868825471235253]], [[0.032068886562107414, 0.0, 0.0], [0.0, 0.032068886562107414, 0.0], [0.0, 0.0, 0.032068886562107414]]], [[[0.02112241425389188, 0.0, 0.0], [0.0, 0.02112241425389188, 0.0], [0.0, 0.0, 0.02112241425389188]], [[0.03206888656210742, 0.0, 0.0], [0.0, 0.03206888656210742, 0.0], [0.0, 0.0, 0.03206888656210742]], [[0.038952921396240715, 0.0, 0.0], [0.0, 0.038952921396240715, 0.0], [0.0, 0.0, 0.038952921396240715]], [[0.03206888656210742, 0.0, 0.0], [0.0, 0.03206888656210742, 0.0], [0.0, 0.0, 0.03206888656210742]], [[0.02112241425389188, 0.0, 0.0], [0.0, 0.02112241425389188, 0.0], [0.0, 0.0, 0.02112241425389188]]]]'
            )),
        dtype=tf.float32
    )  # Expr, id: 139681943153072, Linenos for Expr: [263, 589, 731, 21, 84, 88], Linenos for codegen: [495, 451, 460, 490, 451, 138, 150, 176, 84, 88]
    var001_util_tensor_conv2d = util.tensor_conv2d(
        x, var000
    )  # Expr, id: 139681943600488, Linenos for Expr: [263, 678, 671, 738, 21, 84, 88], Linenos for codegen: [495, 451, 138, 150, 176, 84, 88]
    return tf.cast(var001_util_tensor_conv2d, tf.float32)

Esempio n. 2

def f2(x):
    """
    33x33 kernel
    """
    kernel_size = 33
    kernel_base = numpy.zeros([kernel_size, kernel_size])
    kernel_half = kernel_size // 2
    kernel_base[kernel_half, kernel_half] = 1.0
    kernel = scipy.ndimage.filters.gaussian_filter(kernel_base, 10.0)
    kernel_reshape = numpy.zeros([kernel_size, kernel_size, 3, 3])
    kernel_reshape[:, :, 0, 0] = kernel[:, :]
    kernel_reshape[:, :, 1, 1] = kernel[:, :]
    kernel_reshape[:, :, 2, 2] = kernel[:, :]
    var000 = tf.constant(kernel_reshape, tf.float32)
    #var000 = tf.constant(numpy.asarray(json.loads('[[[[0.02112241425389188, 0.0, 0.0], [0.0, 0.02112241425389188, 0.0], [0.0, 0.0, 0.02112241425389188]], [[0.03206888656210742, 0.0, 0.0], [0.0, 0.03206888656210742, 0.0], [0.0, 0.0, 0.03206888656210742]], [[0.038952921396240715, 0.0, 0.0], [0.0, 0.038952921396240715, 0.0], [0.0, 0.0, 0.038952921396240715]], [[0.03206888656210742, 0.0, 0.0], [0.0, 0.03206888656210742, 0.0], [0.0, 0.0, 0.03206888656210742]], [[0.02112241425389188, 0.0, 0.0], [0.0, 0.02112241425389188, 0.0], [0.0, 0.0, 0.02112241425389188]]], [[[0.032068886562107414, 0.0, 0.0], [0.0, 0.032068886562107414, 0.0], [0.0, 0.0, 0.032068886562107414]], [[0.04868825471235253, 0.0, 0.0], [0.0, 0.04868825471235253, 0.0], [0.0, 0.0, 0.04868825471235253]], [[0.05913986926416638, 0.0, 0.0], [0.0, 0.05913986926416638, 0.0], [0.0, 0.0, 0.05913986926416638]], [[0.04868825471235253, 0.0, 0.0], [0.0, 0.04868825471235253, 0.0], [0.0, 0.0, 0.04868825471235253]], [[0.032068886562107414, 0.0, 0.0], [0.0, 0.032068886562107414, 0.0], [0.0, 0.0, 0.032068886562107414]]], [[[0.038952921396240715, 0.0, 0.0], [0.0, 0.038952921396240715, 0.0], [0.0, 0.0, 0.038952921396240715]], [[0.05913986926416638, 0.0, 0.0], [0.0, 0.05913986926416638, 0.0], [0.0, 0.0, 0.05913986926416638]], [[0.07183506899653452, 0.0, 0.0], [0.0, 0.07183506899653452, 0.0], [0.0, 0.0, 0.07183506899653452]], [[0.05913986926416638, 0.0, 0.0], [0.0, 0.05913986926416638, 0.0], [0.0, 0.0, 0.05913986926416638]], [[0.038952921396240715, 0.0, 0.0], [0.0, 0.038952921396240715, 0.0], [0.0, 0.0, 0.038952921396240715]]], [[[0.032068886562107414, 0.0, 0.0], [0.0, 0.032068886562107414, 0.0], [0.0, 0.0, 0.032068886562107414]], [[0.04868825471235253, 0.0, 0.0], [0.0, 0.04868825471235253, 0.0], [0.0, 0.0, 0.04868825471235253]], [[0.05913986926416638, 0.0, 0.0], [0.0, 0.05913986926416638, 0.0], [0.0, 0.0, 0.05913986926416638]], [[0.04868825471235253, 0.0, 0.0], [0.0, 0.04868825471235253, 0.0], [0.0, 0.0, 0.04868825471235253]], [[0.032068886562107414, 0.0, 0.0], [0.0, 0.032068886562107414, 0.0], [0.0, 0.0, 0.032068886562107414]]], [[[0.02112241425389188, 0.0, 0.0], [0.0, 0.02112241425389188, 0.0], [0.0, 0.0, 0.02112241425389188]], [[0.03206888656210742, 0.0, 0.0], [0.0, 0.03206888656210742, 0.0], [0.0, 0.0, 0.03206888656210742]], [[0.038952921396240715, 0.0, 0.0], [0.0, 0.038952921396240715, 0.0], [0.0, 0.0, 0.038952921396240715]], [[0.03206888656210742, 0.0, 0.0], [0.0, 0.03206888656210742, 0.0], [0.0, 0.0, 0.03206888656210742]], [[0.02112241425389188, 0.0, 0.0], [0.0, 0.02112241425389188, 0.0], [0.0, 0.0, 0.02112241425389188]]]]')), dtype=tf.float32) # Expr, id: 139681943153072, Linenos for Expr: [263, 589, 731, 21, 84, 88], Linenos for codegen: [495, 451, 460, 490, 451, 138, 150, 176, 84, 88]
    var001_util_tensor_conv2d = util.tensor_conv2d(x,var000, filter_size=[kernel_size, kernel_size]) # Expr, id: 139681943600488, Linenos for Expr: [263, 678, 671, 738, 21, 84, 88], Linenos for codegen: [495, 451, 138, 150, 176, 84, 88]
    return tf.cast(var001_util_tensor_conv2d, tf.float32)