diff --git a/docs/tutorials/text_classification_rnn.ipynb b/docs/tutorials/text_classification_rnn.ipynb
index 61538bf5e..f2ee7c458 100644
--- a/docs/tutorials/text_classification_rnn.ipynb
+++ b/docs/tutorials/text_classification_rnn.ipynb
@@ -449,7 +449,7 @@
         "\n",
         "sample_text = ('The movie was cool. The animation and the graphics '\n",
         "               'were out of this world. I would recommend this movie.')\n",
-        "predictions = model.predict(np.array([sample_text]))\n",
+        "predictions = model.predict(tf.constant([sample_text]))\n",
         "print(predictions[0])"
       ]
     },
@@ -473,7 +473,7 @@
         "# predict on a sample text with padding\n",
         "\n",
         "padding = \"the \" * 2000\n",
-        "predictions = model.predict(np.array([sample_text, padding]))\n",
+        "predictions = model.predict(tf.constant([sample_text, padding]))\n",
         "print(predictions[0])"
       ]
     },
@@ -573,7 +573,7 @@
       "source": [
         "sample_text = ('The movie was cool. The animation and the graphics '\n",
         "               'were out of this world. I would recommend this movie.')\n",
-        "predictions = model.predict(np.array([sample_text]))"
+        "predictions = model.predict(tf.constant([sample_text]))"
       ]
     },
     {
@@ -675,7 +675,7 @@
         "\n",
         "sample_text = ('The movie was not good. The animation and the graphics '\n",
         "               'were terrible. I would not recommend this movie.')\n",
-        "predictions = model.predict(np.array([sample_text]))\n",
+        "predictions = model.predict(tf.constant([sample_text]))\n",
         "print(predictions)"
       ]
     },