def build_inputs(parameters, sess, inputs, outputs): input_value0 = create_scalar_data(dtype=np.int32, min_value=1) input_value1 = create_scalar_data(dtype=np.int32, min_value=1) if parameters["use_num_cols"]: return [input_value0, input_value1], sess.run( outputs, feed_dict=dict(zip(inputs, [input_value0, input_value1]))) else: return [input_value0 ], sess.run(outputs, feed_dict=dict(zip(inputs, [input_value0])))
def build_inputs(parameters, sess, inputs, outputs): if parameters["value_is_scalar"] and parameters["value_count"] == 1: input_value = create_scalar_data(parameters["value_dtype"]) else: input_value = create_tensor_data(parameters["value_dtype"], [parameters["value_count"]]) return [input_value], sess.run( outputs, feed_dict=dict(zip(inputs, [input_value])))
def build_inputs(parameters, sess, inputs, outputs): input1 = create_tensor_data(parameters["dims_dtype"], parameters["dims_shape"], 1) input2 = create_scalar_data(parameters["value_dtype"]) return [input1, input2], sess.run(outputs, feed_dict=dict(zip(inputs, [input1, input2])))
def build_inputs(parameters, sess, inputs, outputs): input_value = create_scalar_data(parameters["dtype"]) return [input_value], sess.run( outputs, feed_dict=dict(zip(inputs, [input_value])))