Fixed keras model load and save

photonai/modelwrapper/keras_base_estimator.py

@@ -1,4 +1,5 @@
 import warnings
+import os
 import tensorflow.keras as keras
 from sklearn.base import BaseEstimator
 
@@ -72,20 +73,28 @@ def encode_targets(self, y):
 
     def save(self, filename):
         # serialize model to JSON
+        warnings.warn("Using json export for compatibility, will be deprecated in future.")
         model_json = self.model.to_json()
         with open(filename + ".json", "w") as json_file:
-            json_file.write(model_json)
+           json_file.write(model_json)
         # serialize weights to HDF5
         self.model.save_weights(filename + ".weights.h5")
+        self.model.save(filename + ".keras")
 
     def load(self, filename):
         # load json and create model
-        json_file = open(filename + '.json', 'r')
-        loaded_model_json = json_file.read()
-        json_file.close()
-        loaded_model = keras.models.model_from_json(loaded_model_json)
+        if not os.path.exists(filename+'.keras'):
+            warnings.warn("Using json import for compatiblity, will be deprecated in future. "
+                          "Please save your model to get a *.keras file")
+            json_file = open(filename + '.json', 'r')
+            loaded_model_json = json_file.read()
+            json_file.close()
+            loaded_model = keras.models.model_from_json(loaded_model_json)
+
+            loaded_model.load_weights(filename + ".weights.h5")
+            self.model = loaded_model
+            self.init_weights = self.model.get_weights()
+        else:
+           # load weights into new model
+           self.model = keras.models.load_model(filename + '.keras')
 
-        # load weights into new model
-        loaded_model.load_weights(filename + ".weights.h5")
-        self.model = loaded_model
-        self.init_weights = self.model.get_weights()

test/modelwrapper_tests/test_keras_basic.py

@@ -1,7 +1,7 @@
 from sklearn.datasets import load_breast_cancer, load_diabetes
 import tensorflow as tf
 from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Dense, Dropout
+from tensorflow.keras.layers import Dense, Dropout, Input, Activation
 import numpy as np
 import warnings
 import os
@@ -16,16 +16,17 @@ def setUp(self):
         self.X, self.y = load_breast_cancer(return_X_y=True)
 
         self.model = Sequential()
-        self.model.add(Dense(3, input_dim=self.X.shape[1], activation='relu'))
+        self.model.add(Input(shape=[self.X.shape[1]]))
+        self.model.add(Dense(3, activation="relu"))
         self.model.add(Dropout(0.1))
         self.model.add(Dense(2, activation='softmax'))
         self.model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
 
         self.estimator_type = KerasBaseClassifier
 
         inputs = tf.keras.Input(shape=(self.X.shape[1],))
-        x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
-        outputs = tf.keras.layers.Dense(2, activation=tf.nn.softmax)(x)
+        x = tf.keras.layers.Dense(4, activation=tf.keras.activations.relu)(inputs)
+        outputs = tf.keras.layers.Dense(2, activation=tf.keras.activations.softmax)(x)
         self.tf_model = tf.keras.Model(inputs=inputs, outputs=outputs)
         self.tf_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
 
@@ -57,10 +58,18 @@ def test_tf_model(self):
 
         estimator.save("keras_example_saved_model")
 
-        reload_estinator = self.estimator_type()
-        reload_estinator.load("keras_example_saved_model")
+        reload_estimator = self.estimator_type()
+        reload_estimator.load("keras_example_saved_model")
+
+        np.testing.assert_array_almost_equal(estimator.predict(self.X), reload_estimator.predict(self.X), decimal=3)
+
+        # remove novel keras file and test legacy import
+        os.remove("keras_example_saved_model.keras")
+
+        reload_estimator_legacy = self.estimator_type()
+        reload_estimator_legacy.load("keras_example_saved_model")
 
-        np.testing.assert_array_almost_equal(estimator.predict(self.X), reload_estinator.predict(self.X), decimal=3)
+        np.testing.assert_array_almost_equal(estimator.predict(self.X), reload_estimator.predict(self.X), decimal=3)
 
         # remove saved keras files
         for fname in os.listdir("."):