fix dtype handling in 2 optimizers and 1 layer (#7088)

* fix dtype handling in 2 optimizers and 1 layer

* fix zeros

* Add base_dtype argument

* Fix base_dtype

* remove base_dtype

keras/backend/tensorflow_backend.py

@@ -550,7 +550,7 @@ def dtype(x):
         'float32_ref'
     ```
     """
-    return x.dtype.name
+    return x.dtype.base_dtype.name
 
 
 def eval(x):

keras/layers/embeddings.py

@@ -75,7 +75,6 @@ class Embedding(Layer):
                  mask_zero=False,
                  input_length=None,
                  **kwargs):
-        kwargs['dtype'] = 'int32'
         if 'input_shape' not in kwargs:
             if input_length:
                 kwargs['input_shape'] = (input_length,)
@@ -98,7 +97,8 @@ class Embedding(Layer):
             initializer=self.embeddings_initializer,
             name='embeddings',
             regularizer=self.embeddings_regularizer,
-            constraint=self.embeddings_constraint)
+            constraint=self.embeddings_constraint,
+            dtype=self.dtype)
         self.built = True
 
     def compute_mask(self, inputs, mask=None):

keras/optimizers.py

@@ -219,8 +219,7 @@ class RMSprop(Optimizer):
 
     def get_updates(self, params, constraints, loss):
         grads = self.get_gradients(loss, params)
-        shapes = [K.get_variable_shape(p) for p in params]
-        accumulators = [K.zeros(shape) for shape in shapes]
+        accumulators = [K.zeros(K.get_variable_shape(p), dtype=K.dtype(p)) for p in params]
         self.weights = accumulators
         self.updates = []
 
@@ -413,9 +412,8 @@ class Adam(Optimizer):
         lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
                      (1. - K.pow(self.beta_1, t)))
 
-        shapes = [K.get_variable_shape(p) for p in params]
-        ms = [K.zeros(shape) for shape in shapes]
-        vs = [K.zeros(shape) for shape in shapes]
+        ms = [K.zeros(K.get_variable_shape(p), dtype=K.dtype(p)) for p in params]
+        vs = [K.zeros(K.get_variable_shape(p), dtype=K.dtype(p)) for p in params]
         self.weights = [self.iterations] + ms + vs
 
         for p, g, m, v in zip(params, grads, ms, vs):