def ttrl(x, ranks, n_outputs):
weight_initializer = tf.contrib.layers.xavier_initializer()
if(type(n_outputs) == type([])):
#n_outputs [2,2,2,2,2,2]
#ranks [1,1,2,2,3,4,3,2,2,1,1]
#x [50,14,14,32]
#input_shape[14,14,32,2,2,2,2,2,2]
suffix = np.prod(n_outputs)
input_shape = x.get_shape().as_list()[1:]+n_outputs
bias = tf.get_variable("bias_{}".format(np.prod(n_outputs)), shape=(1, np.prod(n_outputs)))
cores = []
for i in range(len(input_shape)-1):
cores.append(tf.get_variable("core_{0}_output_{1}".format(i,suffix),
shape = (ranks[i], input_shape[i], ranks[i+1]),
initializer = weight_initializer))
cores.append(tf.get_variable("core_{0}_last_output_{1}".format(i,suffix),
shape = (ranks[-2], input_shape[-1], ranks[-1]),
initializer = weight_initializer))
#for c in cores:
# print(c.get_shape().as_list())
regression_weights = TNSR.tt_to_tensor(cores)
w_minus1 = tf.reshape(regression_weights, [np.prod(input_shape[:3]), np.prod(n_outputs)])
x_0 = tf.reshape(x, [-1, np.prod(x.get_shape().as_list()[1:])])
return tf.reshape(tf.add(tf.matmul(x_0, w_minus1) ,bias), [-1]+n_outputs)
else:
suffix = n_outputs
input_shape = x.get_shape().as_list()[1:]
#bias = tf.get_variable("bias", shape=(1, n_outputs))
bias = tf.get_variable("bias_{}".format(np.prod(n_outputs)), shape=(1, np.prod(n_outputs)))
cores = []
for i in range(1, len(ranks)-1):
cores.append(tf.get_variable("core_{0}_output_{1}".format(i,suffix),
shape = (ranks[i-1], input_shape[i-1], ranks[i]),
initializer = weight_initializer))
cores.append(tf.get_variable("core_{0}_last_output_{1}".format(i,suffix),
shape=(ranks[-2],n_outputs,ranks[-1]),
initializer = weight_initializer))
regression_weights = TNSR.tt_to_tensor(cores)
return regression(x, regression_weights, input_shape, bias, n_outputs)
def ttrl(x, ranks, n_outputs):
weight_initializer = tf.contrib.layers.xavier_initializer()
suffix = n_outputs
input_shape = x.get_shape().as_list()[1:]
bias = tf.get_variable("bias_{}".format(np.prod(n_outputs)), shape=(1, np.prod(n_outputs)))
cores = []
for i in range(1, len(ranks)-1):
cores.append(tf.get_variable("core_{0}_output_{1}".format(i,suffix),
shape = (ranks[i-1], input_shape[i-1], ranks[i]),
initializer = weight_initializer))
cores.append(tf.get_variable("core_{0}_last_output_{1}".format(i,suffix),
shape=(ranks[-2],n_outputs,ranks[-1]),
initializer = weight_initializer))
regression_weights = TNSR.tt_to_tensor(cores)
return regression(x, regression_weights, input_shape, bias, n_outputs)
def ttrl(x, ranks, n_outputs):
weight_initializer = tf.contrib.layers.xavier_initializer()
suffix = n_outputs
input_shape = x.get_shape().as_list()[1:]
bias = tf.get_variable("bias_{}".format(np.prod(n_outputs)),
shape=(1, np.prod(n_outputs)))
cores = []
for i in range(1, len(ranks) - 1):
cores.append(
tf.get_variable("core_{0}_output_{1}".format(i, suffix),
shape=(ranks[i - 1], input_shape[i - 1], ranks[i]),
initializer=weight_initializer))
cores.append(
tf.get_variable("core_{0}_last_output_{1}".format(i, suffix),
shape=(ranks[-2], n_outputs, ranks[-1]),
initializer=weight_initializer))
regression_weights = TNSR.tt_to_tensor(cores)
return regression(x, regression_weights, input_shape, bias, n_outputs)