From 7b308be4e2de373cb4117cdc924e9e6ee19f1d6a Mon Sep 17 00:00:00 2001
From: Alexander Munch-Hansen <alexmunchhansen@gmail.com>
Date: Mon, 7 May 2018 22:24:47 +0200
Subject: [PATCH] Different implementations of different speed

---
 tensorflow_impl_tests/eager_main.py  | 41 +++++++++++++++++++++++++
 tensorflow_impl_tests/normal_main.py | 46 ++++++++++++++++++++++++++++
 2 files changed, 87 insertions(+)
 create mode 100644 tensorflow_impl_tests/eager_main.py
 create mode 100644 tensorflow_impl_tests/normal_main.py

diff --git a/tensorflow_impl_tests/eager_main.py b/tensorflow_impl_tests/eager_main.py
new file mode 100644
index 0000000..1b58abc
--- /dev/null
+++ b/tensorflow_impl_tests/eager_main.py
@@ -0,0 +1,41 @@
+import time
+import numpy as np
+import tensorflow as tf
+
+tf.enable_eager_execution()
+
+
+
+output_size = 1
+hidden_size = 40
+input_size = 30
+
+
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Dense(40, activation="sigmoid", input_shape=(1,30)),
+    tf.keras.layers.Dense(1, activation="sigmoid")
+])
+
+input = [0, 2, 0, 0, 0, 0, -5, 0, -3, 0, 0, 0, 5, -5, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, -2, 0, 0, 0, 1, 0]
+
+all_input = np.array([input for _ in range(8500)])
+
+single_in = np.array(input).reshape(1,-1)
+
+
+start = time.time()
+
+all_predictions = model.predict_on_batch(all_input)
+
+print(all_predictions)
+print(time.time() - start)
+
+
+
+start = time.time()
+all_predictions = [model(single_in) for _ in range(8500)]
+
+print(all_predictions[:10])
+print(time.time() - start)
+
diff --git a/tensorflow_impl_tests/normal_main.py b/tensorflow_impl_tests/normal_main.py
new file mode 100644
index 0000000..acfc044
--- /dev/null
+++ b/tensorflow_impl_tests/normal_main.py
@@ -0,0 +1,46 @@
+import tensorflow as tf
+import numpy as np
+import time
+
+class Everything:
+
+    def __init__(self):
+
+        self.output_size = 1
+        self.hidden_size = 40
+        self.input_size = 30
+
+        self.input = tf.placeholder('float', [1, self.input_size])
+
+        xavier_init = tf.contrib.layers.xavier_initializer()
+
+
+        W_1 = tf.get_variable("w_1", (self.input_size, self.hidden_size),
+                              initializer=xavier_init)
+        W_2 = tf.get_variable("w_2", (self.hidden_size, self.output_size),
+                              initializer=xavier_init)
+
+        b_1 = tf.get_variable("b_1", (self.hidden_size,),
+                              initializer=tf.zeros_initializer)
+        b_2 = tf.get_variable("b_2", (self.output_size,),
+                              initializer=tf.zeros_initializer)
+
+        value_after_input = tf.sigmoid(tf.matmul(self.input, W_1) + b_1, name='hidden_layer')
+
+        self.value = tf.sigmoid(tf.matmul(value_after_input, W_2) + b_2, name='output_layer')
+
+    def eval(self):
+        input = np.array([0, 2, 0, 0, 0, 0, -5, 0, -3, 0, 0, 0, 5, -5, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, -2, 0, 0, 0, 1, 0])
+        start = time.time()
+        sess = tf.Session()
+        sess.run(tf.global_variables_initializer())
+        for i in range(8500):
+            val = sess.run(self.value, feed_dict={self.input: input.reshape(1,-1)})
+        print(time.time() - start)
+        print(val)
+
+
+everything = Everything()
+everything.eval()
+
+