CTCModel maj for dealing with latest keras and tensorflow versions

CTCModel.py

@@ -760,10 +760,12 @@ class CTCModel:
             or list of arrays of predictions
             (if the model has multiple outputs).
         """
-        num_samples = self.model_pred._check_num_samples(ins, batch_size,
-                                              steps,
-                                              'steps')
 
+        num_samples = check_num_samples(ins,
+                                        batch_size=batch_size,
+                                        steps=steps,
+                                        steps_name='steps')
+        
         if steps is not None:
             # Step-based predictions.
             # Since we do not know how many samples
@@ -1182,4 +1184,50 @@ def tf_edit_distance(hypothesis, truth, norm=False):
 
     inputs are tf.Sparse_tensors """
 
-    return tf.edit_distance(hypothesis, truth, normalize=norm, name='edit_distance')
\ No newline at end of file
+    return tf.edit_distance(hypothesis, truth, normalize=norm, name='edit_distance')
+
+
+def check_num_samples(ins,
+                      batch_size=None,
+                      steps=None,
+                      steps_name='steps'):
+    """Checks the number of samples provided for training and evaluation.
+    The number of samples is not defined when running with `steps`,
+    in which case the number of samples is set to `None`.
+    # Arguments
+        ins: List of tensors to be fed to the Keras function.
+        batch_size: Integer batch size or `None` if not defined.
+        steps: Total number of steps (batches of samples)
+            before declaring `predict_loop` finished.
+            Ignored with the default value of `None`.
+        steps_name: The public API's parameter name for `steps`.
+    # Raises
+        ValueError: when `steps` is `None` and the attribute `ins.shape`
+        does not exist. Also raises ValueError when `steps` is not `None`
+        and `batch_size` is not `None` because they are mutually
+        exclusive.
+    # Returns
+        When `steps` is `None`, returns the number of samples to be
+        processed based on the size of the first dimension of the
+        first input Numpy array. When `steps` is not `None` and
+        `batch_size` is `None`, returns `None`.
+    # Raises
+        ValueError: In case of invalid arguments.
+    """
+    if steps is not None and batch_size is not None:
+        raise ValueError(
+            'If ' + steps_name + ' is set, the `batch_size` must be None.')
+
+    if not ins or any(K.is_tensor(x) for x in ins):
+        if steps is None:
+            raise ValueError(
+                'If your data is in the form of symbolic tensors, '
+                'you should specify the `' + steps_name + '` argument '
+                '(instead of the `batch_size` argument, '
+                'because symbolic tensors are expected to produce '
+                'batches of input data).')
+        return None
+
+    if hasattr(ins[0], 'shape'):
+        return int(ins[0].shape[0])
+    return None # Edge case where ins == [static_learning_phase]