Integrate sayanpa/dcgan3 into master

Tests/EndToEndTests/CNTKv2Python/Tutorials/CNTK_206_Basic_GAN_test.py → Tests/EndToEndTests/CNTKv2Python/Tutorials/CNTK_206A_Basic_GAN_test.py

@@ -9,17 +9,17 @@
 import numpy as np
 
 abs_path = os.path.dirname(os.path.abspath(__file__))
-notebook = os.path.join(abs_path, "..", "..", "..", "..", "Tutorials", "CNTK_206_Basic_GAN.ipynb")
+notebook = os.path.join(abs_path, "..", "..", "..", "..", "Tutorials", "CNTK_206A_Basic_GAN.ipynb")
 
-def test_cntk_206_basic_gan_noErrors(nb):
+def test_cntk_206A_basic_gan_noErrors(nb):
     errors = [output for cell in nb.cells if 'outputs' in cell
               for output in cell['outputs'] if output.output_type == "error"]
     assert errors == []
 
 expectedEvalErrorByDeviceId = { -1: 2.0, 0: 2.05 }
 expectedEvalErrorAtolByDeviceId = { -1: 0.45, 0: 0.3 }
 
-def test_cntk_206_basic_gan_evalCorrect(nb, device_id):
+def test_cntk_206A_basic_gan_evalCorrect(nb, device_id):
     testCell = [cell for cell in nb.cells
                 if cell.cell_type == 'code' and re.search('# Print the generator loss', cell.source)]
     assert len(testCell) == 1

Tests/EndToEndTests/CNTKv2Python/Tutorials/CNTK_206B_DCGAN_test.py

@@ -0,0 +1,29 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+# Licensed under the MIT license. See LICENSE.md file in the project root
+# for full license information.
+# ==============================================================================
+
+import os
+import re
+import numpy as np
+
+abs_path = os.path.dirname(os.path.abspath(__file__))
+notebook = os.path.join(abs_path, "..", "..", "..", "..", "Tutorials", "CNTK_206B_DCGAN.ipynb")
+notebook_deviceIdsToRun = [0]
+notebook_timeoutSeconds = 900
+
+def test_cntk_206B_dcgan_noErrors(nb):
+    errors = [output for cell in nb.cells if 'outputs' in cell
+              for output in cell['outputs'] if output.output_type == "error"]
+    assert errors == []
+
+expectedEvalError = 2.05
+expectedEvalErrorAtol = 0.45
+
+def test_cntk_206B_dcgan_evalCorrect(nb):
+    testCell = [cell for cell in nb.cells
+                if cell.cell_type == 'code' and re.search('# Print the generator loss', cell.source)]
+    assert len(testCell) == 1
+    m = re.match(r"Training loss of the generator is: (?P<actualEvalError>\d+\.\d+)\r?$", testCell[0].outputs[0]['text'])
+    assert np.isclose(float(m.group('actualEvalError')), expectedEvalError, atol=expectedEvalErrorAtol)

Tools/samples.json

@@ -130,11 +130,19 @@
     {
         "category": ["Image"],
         "name":  "Generative Adversarial Networks (GAN)",
-        "url":  "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_206_Basic_GAN.ipynb",
+        "url":  "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_206A_Basic_GAN.ipynb",
         "description":  "This tutorial is a basic implementation of GAN networks. This allows us generate realistic looking MNIST images with no human input (unsupervised learning).",
         "language":  ["Python"],
         "type":  ["Tutorial", "Recipe"]
     },
+    {
+        "category": ["Image"],
+        "name":  "Deep Convolutional Generative Adversarial Networks (DCGAN)",
+        "url":  "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_206B_DCGAN.ipynb",
+        "description":  "This tutorial is an implementation of deep convolutional GAN (DCGAN) networks. This allows us generate realistic looking MNIST images with no human input (unsupervised learning).",
+        "language":  ["Python"],
+        "type":  ["Tutorial", "Recipe"]
+    },
     {
         "category": ["Image"],
         "name":  "One Conv",

Tutorials/CNTK_206_Basic_GAN.ipynb → Tutorials/CNTK_206A_Basic_GAN.ipynb

@@ -15,7 +15,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# CNTK 206: Basic GAN with MNIST data\n",
+    "# CNTK 206 Part A: Basic GAN with MNIST data\n",
     "\n",
     "**Prerequisites**: We assume that you have successfully downloaded the MNIST data by completing the tutorial titled CNTK_103A_MNIST_DataLoader.ipynb.\n",
     "\n",
@@ -749,7 +749,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },