Fix

brainpy/_src/analysis/highdim/slow_points.py

@@ -329,7 +329,7 @@ def find_fps_with_gd_method(
     """
     # optimization settings
     if optimizer is None:
-      optimizer = optim.Adam(lr=optim.ExponentialDecay(0.2, 1, 0.9999),
+      optimizer = optim.Adam(lr=optim.ExponentialDecayLR(0.2, 1, 0.9999),
                              beta1=0.9, beta2=0.999, eps=1e-8)
     else:
       if not isinstance(optimizer, optim.Optimizer):

brainpy/_src/analysis/lowdim/tests/test_bifurcation.py

@@ -39,7 +39,7 @@ def dw(w, t, V, a=0.7, b=0.8):
     self.int_w = bp.odeint(dw, method=method)
 
   def update(self, tdi):
-    t, dt = tdi['t'], tdi['dt']
+    t, dt = bp.share['t'], bp.share['dt']
     self.V.value = self.int_V(self.V, t, self.w, self.Iext, dt)
     self.w.value = self.int_w(self.w, t, self.V, self.a, self.b, dt)
     self.Iext[:] = 0.

brainpy/_src/dyn/rates/tests/test_nvar.py

@@ -11,7 +11,7 @@ class Test_NVAR(parameterized.TestCase):
     def test_NVAR(self,mode):
         bm.random.seed()
         input=bm.random.randn(1,5)
-        layer=bp.dnn.NVAR(num_in=5,
+        layer=bp.dyn.NVAR(num_in=5,
                           delay=10,
                           mode=mode)
         if mode in [bm.NonBatchingMode()]:

brainpy/_src/initialize/tests/test_decay_inits.py

@@ -14,8 +14,8 @@
 # visualization
 def mat_visualize(matrix, cmap=None):
   if cmap is None:
-    cmap = plt.cm.get_cmap('coolwarm')
-  plt.cm.get_cmap('coolwarm')
+    cmap = plt.colormaps.get_cmap('coolwarm')
+  plt.colormaps.get_cmap('coolwarm')
   im = plt.matshow(matrix, cmap=cmap)
   plt.colorbar(mappable=im, shrink=0.8, aspect=15)
   plt.show()

brainpy/_src/integrators/ode/tests/test_ode_method_exp_euler.py

@@ -95,7 +95,7 @@ def dV(self, V, t, h, n, Iext):
         return dVdt
 
       def update(self, tdi):
-        t, dt = tdi.t, tdi.dt
+        t, dt = bp.share['t'], bp.share['dt']
         V, h, n = self.integral(self.V, self.h, self.n, t, self.input, dt=dt)
         self.spike.value = bm.logical_and(self.V < self.V_th, V >= self.V_th)
         self.V.value = V

brainpy/_src/math/object_transform/tests/test_circular_reference.py

@@ -65,7 +65,7 @@ def test_nodes():
   A.pre = B
   B.pre = A
 
-  net = bp.dyn.Network(A, B)
+  net = bp.Network(A, B)
   abs_nodes = net.nodes(method='absolute')
   rel_nodes = net.nodes(method='relative')
   print()

brainpy/_src/math/object_transform/tests/test_collector.py

@@ -7,7 +7,7 @@
 import brainpy as bp
 
 
-class GABAa_without_Variable(bp.TwoEndConn):
+class GABAa_without_Variable(bp.synapses.TwoEndConn):
   def __init__(self, pre, post, conn, delay=0., g_max=0.1, E=-75.,
                alpha=12., beta=0.1, T=1.0, T_duration=1.0, **kwargs):
     super(GABAa_without_Variable, self).__init__(pre=pre, post=post, **kwargs)
@@ -192,7 +192,7 @@ def test_neu_nodes_1():
   assert len(neu.nodes(method='relative', include_self=False)) == 1
 
 
-class GABAa_with_Variable(bp.TwoEndConn):
+class GABAa_with_Variable(bp.synapses.TwoEndConn):
   def __init__(self, pre, post, conn, delay=0., g_max=0.1, E=-75.,
                alpha=12., beta=0.1, T=1.0, T_duration=1.0, **kwargs):
     super(GABAa_with_Variable, self).__init__(pre=pre, post=post, **kwargs)

brainpy/_src/math/object_transform/tests/test_controls.py

@@ -234,7 +234,6 @@ def f1():
                        branches=[f1,
                                  lambda: 2, lambda: 3,
                                  lambda: 4, lambda: 5],
-                       dyn_vars=var_a,
                        show_code=True)
 
     self.assertTrue(f(11) == 1)

brainpy/_src/optimizers/tests/test_ModifyLr.py

@@ -28,7 +28,7 @@ def train_data():
 class RNN(bp.DynamicalSystem):
   def __init__(self, num_in, num_hidden):
     super(RNN, self).__init__()
-    self.rnn = bp.dnn.RNNCell(num_in, num_hidden, train_state=True)
+    self.rnn = bp.dyn.RNNCell(num_in, num_hidden, train_state=True)
     self.out = bp.dnn.Dense(num_hidden, 1)
 
   def update(self, x):

brainpy/_src/train/back_propagation.py

@@ -111,7 +111,7 @@ def __init__(
 
     # optimizer
     if optimizer is None:
-      lr = optim.ExponentialDecay(lr=0.025, decay_steps=1, decay_rate=0.99975)
+      lr = optim.ExponentialDecayLR(lr=0.025, decay_steps=1, decay_rate=0.99975)
       optimizer = optim.Adam(lr=lr)
     self.optimizer: optim.Optimizer = optimizer
     if len(self.optimizer.vars_to_train) == 0:

examples/dynamics_analysis/3d_reduced_trn_model.py

@@ -192,7 +192,7 @@ def derivative(self, V, y, z, t, Isyn):
     return dvdt, dydt, dzdt
 
   def update(self, tdi):
-    t, dt = tdi['t'], tdi['dt']
+    t, dt = bp.share['t'], bp.share['dt']
     if isinstance(self.int_V, bp.ode.ExponentialEuler):
       V = self.int_V(self.V, t, self.y, self.z, self.input, dt)
       self.y.value = self.int_y(self.y, t, self.V, dt)

examples/dynamics_analysis/highdim_RNN_Analysis.py

@@ -151,7 +151,7 @@ def loss(predictions, targets):
   tolerance=1e-5,
   num_batch=200,
   num_opt=int(2e4),
-  optimizer=bp.optim.Adam(lr=bp.optim.ExponentialDecay(0.01, 2, 0.9999)),
+  optimizer=bp.optim.Adam(lr=bp.optim.ExponentialDecayLR(0.01, 2, 0.9999)),
 )
 finder.filter_loss(tolerance=1e-5)
 finder.keep_unique(tolerance=0.005)

examples/training_ann_models/mnist_ResNet.py

@@ -222,7 +222,7 @@ def loss_fun(X, Y, fit=True):
   grad_fun = bm.grad(loss_fun, grad_vars=net.train_vars().unique(), has_aux=True, return_value=True)
 
   # optimizer
-  optimizer = bp.optim.Adam(bp.optim.ExponentialDecay(args.lr, 1, 0.9999),
+  optimizer = bp.optim.Adam(bp.optim.ExponentialDecayLR(args.lr, 1, 0.9999),
                             train_vars=net.train_vars().unique())
 
   @bm.jit