Integrate sayanpa/playgithub-master into master

Tests/EndToEndTests/CNTKv2Python/Tutorials/CNTK_106A_LSTM_Timeseries_with_Simulated_Data_test.py

@@ -23,4 +23,4 @@ def test_cntk_106A_lstm_timeseries_with_simulated_data_evalCorrect(nb, device_id
                 if cell.cell_type == 'code' and re.search('# Print validate and test error', cell.source)]
     assert len(testCell) == 1
     m = re.match(r"mse for test: (?P<actualEvalError>\d+\.\d+)\r?$", testCell[0].outputs[0]['text'])
-    assert np.isclose(float(m.group('actualEvalError')), expectedEvalErrorByDeviceId[device_id], atol=0.000001)
+    assert np.isclose(float(m.group('actualEvalError')), expectedEvalErrorByDeviceId[device_id], atol=0.000004)

Tutorials/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb

@@ -192,7 +192,7 @@
     "        data = pd.DataFrame(dict(a = data[0:len(data) - time_shift],\n",
     "                                 b = data[time_shift:]))\n",
     "    rnn_x = []\n",
-    "    for i in range(len(data) - time_steps):\n",
+    "    for i in range(len(data) - time_steps + 1):\n",
     "        rnn_x.append(data['a'].iloc[i: i + time_steps].as_matrix())\n",
     "    rnn_x = np.array(rnn_x)\n",
     "\n",
@@ -202,6 +202,7 @@
     "    rnn_x = rnn_x.reshape(rnn_x.shape + (1,))\n",
     "    \n",
     "    rnn_y = data['b'].values\n",
+    "    rnn_y = rnn_y[time_steps - 1 :]\n",
     "    \n",
     "    # Reshape or rearrange the data from row to columns\n",
     "    # to match the input shape\n",