Update biRNN.ipynb

Author: YJango

biRNN.ipynb

@@ -47,7 +47,7 @@
     "        return iterator.get_next()\n",
     "    return input_fn\n",
     "\n",
-    "padding_info = ({'image':[28,28,1],'label':[]})\n",
+    "padding_info = ({'image':[784,],'label':[]})\n",
     "test_input_fn = input_fn_maker('mnist_tfrecord/test/',  'mnist_tfrecord/data_info.csv',batch_size = 512,\n",
     "                               padding = padding_info)\n",
     "train_input_fn = input_fn_maker('mnist_tfrecord/train/',  'mnist_tfrecord/data_info.csv', shuffle=True, batch_size = 128,\n",
@@ -63,10 +63,11 @@
    "outputs": [],
    "source": [
     "def model_fn(features, mode):\n",
+    "    features['image'] = tf.reshape(features['image'],[-1,28,28,1])\n",
     "    # shape: [None,28,28,1]\n",
     "    # create RNN cells:\n",
-    "    rnn_fcells = [tf.nn.rnn_cell.GRUCell(dim) for dim in [128,256]]\n",
-    "    rnn_bcells = [tf.nn.rnn_cell.GRUCell(dim) for dim in [128,256]]\n",
+    "    rnn_fcells = [tf.nn.rnn_cell.GRUCell(dim,kernel_initializer=tf.orthogonal_initializer) for dim in [128,256]]\n",
+    "    rnn_bcells = [tf.nn.rnn_cell.GRUCell(dim,kernel_initializer=tf.orthogonal_initializer) for dim in [128,256]]\n",
     "    # stack cells for multi-layers RNN\n",
     "    multi_rnn_fcell = tf.nn.rnn_cell.MultiRNNCell(rnn_fcells)\n",
     "    multi_rnn_bcell = tf.nn.rnn_cell.MultiRNNCell(rnn_bcells)\n",