Fix lab1 assertion dense layer output assertion

1) tf.random.set_seed only sets the global seed https://www.tensorflow.org/api_docs/python/tf/random/set_seed
2) we need to set keras set_random_seed to make all seeds be deterministic. docs : https://www.tensorflow.org/api_docs/python/tf/config/experimental/enable_op_determinism, https://www.tensorflow.org/api_docs/python/tf/keras/utils/set_random_seed.

enable_op_determinism might not be needed here but its good to have.

fixes #116

lab1/Part1_TensorFlow.ipynb

@@ -366,8 +366,11 @@
         "    y = # TODO\n",
         "    return y\n",
         "\n",
-        "# Since layer parameters are initialized randomly, we will set a random seed for reproducibility\n",
-        "tf.random.set_seed(1)\n",
+        "# Since layer parameters are initialized randomly, we will set random seed for reproducibility \n",
+        "# and make operations deterministic\n",
+        "tf.keras.utils.set_random_seed(1)\n",
+        "tf.config.experimental.enable_op_determinism()\n",
+        "\n",
         "layer = OurDenseLayer(3)\n",
         "layer.build((1,2))\n",
         "x_input = tf.constant([[1,2.]], shape=(1,2))\n",

mitdeeplearning/lab1.py

@@ -80,8 +80,8 @@ def test_batch_func_next_step(func, args):
     return True
 
 def test_custom_dense_layer_output(y):
-    true_y = np.array([[0.2697859,  0.45750418, 0.66536945]],dtype='float32')
+    true_y = np.array([[0.27064407, 0.1826951, 0.50374055]],dtype='float32')
     assert tf.shape(y).numpy().tolist() == list(true_y.shape), "[FAIL] output is of incorrect shape. expected {} but got {}".format(true_y.shape, y.numpy().shape)
-    np.testing.assert_almost_equal(y.numpy(), true_y, decimal=7, err_msg="[FAIL] output is of incorrect value. expected {} but got {}".format(y.numpy(), true_y), verbose=True)
+    np.testing.assert_almost_equal(y.numpy(), true_y, decimal=7, err_msg="[FAIL] output is of incorrect value. expected {} but got {}".format(true_y,y.numpy()), verbose=True)
     print("[PASS] test_custom_dense_layer_output")
     return True