Add TF1 platform, docker and README

src/tf_1/nn_dense_x5.py

@@ -0,0 +1,35 @@
+from pathlib import Path
+from typing import List, Tuple
+
+import tensorflow.compat.v1 as tf
+
+from src.common import DataType, Op
+from src.tf_1.base import TFBase
+
+
+class TFNNDenseX5Bench(TFBase):
+    def __init__(self, output_path: Path, data_type: DataType):
+        super().__init__(output_path, Op.NN_DENSE_X5, data_type)
+        self.dense_op = None
+
+    def pre_experiment(self, experiment_args: Tuple[int, int]):
+        super().pre_experiment(experiment_args)
+        batch_size, dimension = experiment_args
+        input_tensor = tf.get_variable('input_tensor', shape=(batch_size, dimension), dtype=self.dtype,
+                                       initializer=tf.initializers.ones, trainable=False)
+        output_tensor = input_tensor
+        for layer in range(5):
+            weights = tf.get_variable(f'Weights_{layer}', shape=(dimension, dimension), dtype=self.dtype,
+                                      initializer=tf.initializers.ones, trainable=False)
+            biases = tf.get_variable(f'Biases_{layer}', shape=dimension, dtype=self.dtype,
+                                     initializer=tf.initializers.ones, trainable=False)
+            output_tensor = tf.matmul(output_tensor, weights) + biases
+        self.dense_op = output_tensor
+
+        self.session.run(tf.initializers.global_variables())
+
+    def experiment(self):
+        self.session.run(self.dense_op)
+
+    def run(self, experiment_args: List[Tuple[int, int]], experiment_count: int):
+        super().run(experiment_args, experiment_count)