Fix example

examples/vision/captcha_ocr.py

@@ -37,9 +37,6 @@
 import keras
 from keras import layers
 
-# TODO restore traceback filtering.
-keras.config.disable_traceback_filtering()
-
 """
 ## Load the data: [Captcha Images](https://www.kaggle.com/fournierp/captcha-version-2-images)
 Let's download the data.
@@ -357,9 +354,33 @@ def build_model():
 """
 
 
+def ctc_decode(y_pred, input_length, greedy=True, beam_width=100, top_paths=1):
+    input_shape = tf.shape(y_pred)
+    num_samples, num_steps = input_shape[0], input_shape[1]
+    y_pred = tf.math.log(tf.transpose(y_pred, perm=[1, 0, 2]) + keras.backend.epsilon())
+    input_length = tf.cast(input_length, tf.int32)
+
+    if greedy:
+        (decoded, log_prob) = tf.nn.ctc_greedy_decoder(
+            inputs=y_pred, sequence_length=input_length
+        )
+    else:
+        (decoded, log_prob) = tf.compat.v1.nn.ctc_beam_search_decoder(
+            inputs=y_pred,
+            sequence_length=input_length,
+            beam_width=beam_width,
+            top_paths=top_paths,
+        )
+    decoded_dense = []
+    for st in decoded:
+        st = tf.SparseTensor(st.indices, st.values, (num_samples, num_steps))
+        decoded_dense.append(tf.sparse.to_dense(sp_input=st, default_value=-1))
+    return (decoded_dense, log_prob)
+
+
 # Get the prediction model by extracting layers till the output layer
 prediction_model = keras.models.Model(
-    model.get_layer(name="image").input, model.get_layer(name="dense2").output
+    model.input[0], model.get_layer(name="dense2").output
 )
 prediction_model.summary()
 
@@ -368,7 +389,7 @@ def build_model():
 def decode_batch_predictions(pred):
     input_len = np.ones(pred.shape[0]) * pred.shape[1]
     # Use greedy search. For complex tasks, you can use beam search
-    results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0][
+    results = ctc_decode(pred, input_length=input_len, greedy=True)[0][0][
         :, :max_length
     ]
     # Iterate over the results and get back the text

examples/vision/ipynb/captcha_ocr.ipynb

@@ -60,9 +60,7 @@
     "\n",
     "import tensorflow as tf\n",
     "import keras\n",
-    "from keras import layers\n",
-    "\n",
-    "keras.config.disable_traceback_filtering()"
+    "from keras import layers"
    ]
   },
   {
@@ -450,6 +448,7 @@
    "outputs": [],
    "source": [
     "\n",
+    "# TODO restore epoch count.\n",
     "epochs = 2\n",
     "early_stopping_patience = 10\n",
     "# Add early stopping\n",
@@ -487,10 +486,34 @@
    },
    "outputs": [],
    "source": [
+    "\n",
+    "def ctc_decode(y_pred, input_length, greedy=True, beam_width=100, top_paths=1):\n",
+    "    input_shape = tf.shape(y_pred)\n",
+    "    num_samples, num_steps = input_shape[0], input_shape[1]\n",
+    "    y_pred = tf.math.log(tf.transpose(y_pred, perm=[1, 0, 2]) + keras.backend.epsilon())\n",
+    "    input_length = tf.cast(input_length, tf.int32)\n",
+    "\n",
+    "    if greedy:\n",
+    "        (decoded, log_prob) = tf.nn.ctc_greedy_decoder(\n",
+    "            inputs=y_pred, sequence_length=input_length\n",
+    "        )\n",
+    "    else:\n",
+    "        (decoded, log_prob) = tf.compat.v1.nn.ctc_beam_search_decoder(\n",
+    "            inputs=y_pred,\n",
+    "            sequence_length=input_length,\n",
+    "            beam_width=beam_width,\n",
+    "            top_paths=top_paths,\n",
+    "        )\n",
+    "    decoded_dense = []\n",
+    "    for st in decoded:\n",
+    "        st = tf.SparseTensor(st.indices, st.values, (num_samples, num_steps))\n",
+    "        decoded_dense.append(tf.sparse.to_dense(sp_input=st, default_value=-1))\n",
+    "    return (decoded_dense, log_prob)\n",
+    "\n",
     "\n",
     "# Get the prediction model by extracting layers till the output layer\n",
     "prediction_model = keras.models.Model(\n",
-    "    model.get_layer(name=\"image\").input, model.get_layer(name=\"dense2\").output\n",
+    "    model.input[0], model.get_layer(name=\"dense2\").output\n",
     ")\n",
     "prediction_model.summary()\n",
     "\n",
@@ -499,7 +522,7 @@
     "def decode_batch_predictions(pred):\n",
     "    input_len = np.ones(pred.shape[0]) * pred.shape[1]\n",
     "    # Use greedy search. For complex tasks, you can use beam search\n",
-    "    results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0][\n",
+    "    results = ctc_decode(pred, input_length=input_len, greedy=True)[0][0][\n",
     "        :, :max_length\n",
     "    ]\n",
     "    # Iterate over the results and get back the text\n",