Compatible with Keras 2.3.0

CyberZHG · Sep 18, 2019 · de55316 · de55316
1 parent 3dbdc01
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diff --git a/.travis.yml b/.travis.yml
@@ -1,13 +1,15 @@
 dist: xenial
 language: python
-python:
-  - 3.6
+python: "3.6"
 env:
-  - KERAS_BACKEND=tensorflow TF_KERAS=1 TF_2=1
-  - KERAS_BACKEND=tensorflow TF_KERAS=1 TF_EAGER=1
-  - KERAS_BACKEND=tensorflow TF_KERAS=1
-  - KERAS_BACKEND=tensorflow
-  - KERAS_BACKEND=theano THEANO_FLAGS=optimizer=fast_compile
+  global:
+    - COVERALLS_PARALLEL=true
+  matrix:
+    - KERAS_BACKEND=tensorflow TF_KERAS=1 TF_2=1
+    - KERAS_BACKEND=tensorflow TF_KERAS=1 TF_EAGER=1
+    - KERAS_BACKEND=tensorflow TF_KERAS=1
+    - KERAS_BACKEND=tensorflow
+    - KERAS_BACKEND=theano THEANO_FLAGS=optimizer=fast_compile
 install:
   - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
   - bash miniconda.sh -b -p $HOME/miniconda

diff --git a/README.md b/README.md
@@ -22,6 +22,10 @@ Unofficial implementation of the [lookahead mechanism](https://arxiv.org/pdf/190
 pip install keras-lookahead
 ```
 
+## External Links
+
+- [tensorflow/addons:LookAhead](https://github.com/tensorflow/addons/blob/master/tensorflow_addons/optimizers/lookahead.py)
+
 ## Usage
 
 Arguments:

diff --git a/README.zh-CN.md b/README.zh-CN.md
@@ -22,6 +22,10 @@
 pip install keras-lookahead
 ```
 
+## 外部链接
+
+- [tensorflow/addons:LookAhead](https://github.com/tensorflow/addons/blob/master/tensorflow_addons/optimizers/lookahead.py)
+
 ## 使用
 
 参数：

diff --git a/keras_lookahead/optimizers.py b/keras_lookahead/optimizers.py
@@ -34,12 +34,20 @@ def lr(self):
     def lr(self, lr):
         self.optimizer.lr = lr
 
+    @property
+    def learning_rate(self):
+        return self.optimizer.learning_rate
+
+    @learning_rate.setter
+    def learning_rate(self, learning_rate):
+        self.optimizer.learning_rate = learning_rate
+
     @property
     def iterations(self):
         return self.optimizer.iterations
 
     def get_updates(self, loss, params):
-        sync_cond = K.equal((self.iterations + 1) % self.sync_period, 0)
+        sync_cond = K.equal((self.iterations + 1) // self.sync_period * self.sync_period, (self.iterations + 1))
         if TF_KERAS:
             slow_params = [K.variable(K.get_value(p), name='sp_{}'.format(i)) for i, p in enumerate(params)]
             self.updates = self.optimizer.get_updates(loss, params)

diff --git a/setup.py b/setup.py
@@ -11,7 +11,7 @@
 
 setup(
     name='keras-lookahead',
-    version='0.5.0',
+    version='0.6.0',
     packages=find_packages(),
     url='https://github.com/CyberZHG/keras-lookahead',
     license='MIT',

diff --git a/tests/optimizers.py b/tests/optimizers.py
@@ -8,7 +8,7 @@ def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999, **kwargs):
         super(Adam, self).__init__(**kwargs)
         with K.name_scope(self.__class__.__name__):
             self.iterations = K.variable(0, dtype='int64', name='iterations')
-            self.lr = K.variable(lr, name='lr')
+            self.learning_rate = K.variable(lr, name='lr')
             self.beta_1 = K.variable(beta_1, name='beta_1')
             self.beta_2 = K.variable(beta_2, name='beta_2')
         self.epsilon = K.epsilon()
@@ -18,7 +18,7 @@ def get_updates(self, loss, params):
         self.updates = [K.update_add(self.iterations, 1)]
 
         t = K.cast(self.iterations, K.floatx()) + 1
-        lr_t = self.lr * (K.sqrt(1. - K.pow(self.beta_2, t)) / (1. - K.pow(self.beta_1, t)))
+        lr_t = self.learning_rate * (K.sqrt(1. - K.pow(self.beta_2, t)) / (1. - K.pow(self.beta_1, t)))
 
         ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
         vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]

diff --git a/tests/test_optimizers.py b/tests/test_optimizers.py
@@ -61,22 +61,23 @@ def test_ranger(self):
         self.assertLess(np.max(np.abs(predicted - y)), 1e-3)
 
     def test_half(self):
-        weight = np.random.standard_normal((5, 1))
-        x, y, _ = self._init_data(data_size=3200)
+        if TF_KERAS:
+            weight = np.random.standard_normal((5, 1))
+            x, y, _ = self._init_data(data_size=3200)
 
-        model = self._init_model('adam', w=weight)
-        model.fit(x, y, batch_size=32)
-        original = model.get_weights()[0]
+            model = self._init_model('adam', w=weight)
+            model.fit(x, y, batch_size=32)
+            original = model.get_weights()[0]
 
-        model = self._init_model(Lookahead('adam', sync_period=100, slow_step=0.5), w=weight)
-        model.fit(x, y, batch_size=32)
-        step_back = model.get_weights()[0]
+            model = self._init_model(Lookahead('adam', sync_period=100, slow_step=0.5), w=weight)
+            model.fit(x, y, batch_size=32)
+            step_back = model.get_weights()[0]
 
-        half_step = (weight + original) * 0.5
-        self.assertTrue(np.allclose(half_step, step_back, atol=1e-2))
+            half_step = (weight + original) * 0.5
+            self.assertTrue(np.allclose(half_step, step_back, atol=1e-2), (weight, original, step_back, half_step))
 
     def test_lr(self):
-        opt = Lookahead('adam')
+        opt = Lookahead(RAdam())
         K.set_value(opt.lr, 1e-4)
         self.assertAlmostEqual(1e-4, K.get_value(opt.lr))
         self.assertAlmostEqual(1e-4, K.get_value(opt.optimizer.lr))
@@ -118,4 +119,4 @@ def test_consistent(self):
         model.fit(x, y, batch_size=32, shuffle=False)
         loaded = model.get_weights()[0]
 
-        self.assertTrue(np.allclose(original, loaded, atol=1e-6))
+        self.assertTrue(np.allclose(original, loaded, atol=1e-4), (original, loaded))