# Path: t3f/nn.py
# class KerasDense(Layer):
#   def __init__(self, input_dims, output_dims, tt_rank=2,
#                activation=None, use_bias=True, kernel_initializer='glorot',
#                bias_initializer=0.1, **kwargs):
#   def call(self, x):
#   def compute_output_shape(self, input_shape):


import numpy as np
import tensorflow as tf
from t3f import nn
tf.compat.v1.enable_v2_behavior()



class _NeuralTest():

  def testKerasDense(self):
    # Try to create the layer twice to check that it won't crush saying the
    # variable already exist.
    x = tf.random.normal((20, 28*28))
    layer = nn.KerasDense(input_dims=[7, 4, 7, 4], output_dims=[5, 5, 5, 5])
    layer(x)

# Path: t3f/nn.py
# class KerasDense(Layer):
#   def __init__(self, input_dims, output_dims, tt_rank=2,
#                activation=None, use_bias=True, kernel_initializer='glorot',
#                bias_initializer=0.1, **kwargs):
#   def call(self, x):
#   def compute_output_shape(self, input_shape):


# Path: t3f/nn_test.py
import numpy as np
import tensorflow as tf
from t3f import nn

tf.compat.v1.enable_v2_behavior()



class _NeuralTest():

  def testKerasDense(self):
    # Try to create the layer twice to check that it won't crush saying the
    # variable already exist.
    x = tf.random.normal((20, 28*28))
    layer = nn.KerasDense(input_dims=[7, 4, 7, 4], output_dims=[5, 5, 5, 5])
    layer(x)

    layer = nn.KerasDense(input_dims=[7, 4, 7, 4], output_dims=[5, 5, 5, 5])