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Merged
merged 2 commits into from
Jul 1, 2022
Merged

fix bugs #235

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17a3228
fix bugs
chaoming0625 Jul 1, 2022
3b65324
fix bugs
chaoming0625 Jul 1, 2022
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31 changes: 18 additions & 13 deletions brainpy/dyn/synapses/abstract_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -128,7 +128,7 @@ def __init__(

def reset_state(self, batch_size=None):
self.output.reset_state(batch_size)
self.stp.reset_state(batch_size)
if self.stp is not None: self.stp.reset_state(batch_size)

def update(self, tdi, pre_spike=None):
# pre-synaptic spikes
Expand All @@ -140,7 +140,7 @@ def update(self, tdi, pre_spike=None):

# update sub-components
self.output.update(tdi)
self.stp.update(tdi, pre_spike)
if self.stp is not None: self.stp.update(tdi, pre_spike)

# synaptic values onto the post
if isinstance(self.conn, All2All):
Expand Down Expand Up @@ -312,7 +312,7 @@ def __init__(
def reset_state(self, batch_size=None):
self.g.value = variable(bm.zeros, batch_size, self.post.num)
self.output.reset_state(batch_size)
self.stp.reset_state(batch_size)
if self.stp is not None: self.stp.reset_state(batch_size)

def update(self, tdi, pre_spike=None):
t, dt = tdi['t'], tdi['dt']
Expand All @@ -326,14 +326,16 @@ def update(self, tdi, pre_spike=None):

# update sub-components
self.output.update(tdi)
self.stp.update(tdi, pre_spike)
if self.stp is not None: self.stp.update(tdi, pre_spike)

# post values
if isinstance(self.conn, All2All):
syn_value = self.stp(bm.asarray(pre_spike, dtype=bm.dftype()))
syn_value = bm.asarray(pre_spike, dtype=bm.dftype())
if self.stp is not None: syn_value = self.stp(syn_value)
post_vs = self.syn2post_with_all2all(syn_value, self.g_max)
elif isinstance(self.conn, One2One):
syn_value = self.stp(bm.asarray(pre_spike, dtype=bm.dftype()))
syn_value = bm.asarray(pre_spike, dtype=bm.dftype())
if self.stp is not None: syn_value = self.stp(syn_value)
post_vs = self.syn2post_with_one2one(syn_value, self.g_max)
else:
if self.comp_method == 'sparse':
Expand All @@ -343,7 +345,8 @@ def update(self, tdi, pre_spike=None):
# if not isinstance(self.stp, _NullSynSTP):
# raise NotImplementedError()
else:
syn_value = self.stp(bm.asarray(pre_spike, dtype=bm.dftype()))
syn_value = bm.asarray(pre_spike, dtype=bm.dftype())
if self.stp is not None: syn_value = self.stp(syn_value)
post_vs = self.syn2post_with_dense(syn_value, self.g_max, self.conn_mask)
# updates
self.g.value = self.integral(self.g.value, t, dt) + post_vs
Expand Down Expand Up @@ -503,7 +506,7 @@ def reset_state(self, batch_size=None):
self.h.value = variable(bm.zeros, batch_size, self.pre.num)
self.g.value = variable(bm.zeros, batch_size, self.pre.num)
self.output.reset_state(batch_size)
self.stp.reset_state(batch_size)
if self.stp is not None: self.stp.reset_state(batch_size)

def dh(self, h, t):
return -h / self.tau_rise
Expand All @@ -523,14 +526,15 @@ def update(self, tdi, pre_spike=None):

# update sub-components
self.output.update(tdi)
self.stp.update(tdi, pre_spike)
if self.stp is not None: self.stp.update(tdi, pre_spike)

# update synaptic variables
self.g.value, self.h.value = self.integral(self.g, self.h, t, dt)
self.h += pre_spike

# post values
syn_value = self.stp(self.g)
syn_value = self.g.value
if self.stp is not None: syn_value = self.stp(syn_value)
if isinstance(self.conn, All2All):
post_vs = self.syn2post_with_all2all(syn_value, self.g_max)
elif isinstance(self.conn, One2One):
Expand Down Expand Up @@ -912,7 +916,7 @@ def reset_state(self, batch_size=None):
self.g.value = variable(bm.zeros, batch_size, self.pre.num)
self.x.value = variable(bm.zeros, batch_size, self.pre.num)
self.output.reset_state(batch_size)
self.stp.reset_state(batch_size)
if self.stp is not None: self.stp.reset_state(batch_size)

def update(self, tdi, pre_spike=None):
t, dt = tdi['t'], tdi['dt']
Expand All @@ -924,15 +928,16 @@ def update(self, tdi, pre_spike=None):
pre_spike = stop_gradient(pre_spike)

# update sub-components
self.stp.update(tdi, pre_spike)
self.output.update(tdi)
if self.stp is not None: self.stp.update(tdi, pre_spike)

# update synapse variables
self.g.value, self.x.value = self.integral(self.g, self.x, t, dt=dt)
self.x += pre_spike

# post-synaptic value
syn_value = self.stp(self.g)
syn_value = self.g.value
if self.stp is not None: syn_value = self.stp(syn_value)
if isinstance(self.conn, All2All):
post_vs = self.syn2post_with_all2all(syn_value, self.g_max)
elif isinstance(self.conn, One2One):
Expand Down
14 changes: 8 additions & 6 deletions brainpy/dyn/synapses/biological_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -202,7 +202,7 @@ def reset_state(self, batch_size=None):
self.g = variable(bm.zeros, batch_size, self.pre.num)
self.spike_arrival_time = variable(lambda s: bm.ones(s) * -1e7, batch_size, self.pre.num)
self.output.reset_state(batch_size)
self.stp.reset_state(batch_size)
if self.stp is not None: self.stp.reset_state(batch_size)

def dg(self, g, t, TT):
dg = self.alpha * TT * (1 - g) - self.beta * g
Expand All @@ -220,7 +220,7 @@ def update(self, tdi, pre_spike=None):

# update sub-components
self.output.update(tdi)
self.stp.update(tdi, pre_spike)
if self.stp is not None: self.stp.update(tdi, pre_spike)

# update synaptic variables
self.spike_arrival_time.value = bm.where(pre_spike, t, self.spike_arrival_time)
Expand All @@ -230,7 +230,8 @@ def update(self, tdi, pre_spike=None):
self.g.value = self.integral(self.g, t, TT, dt)

# post-synaptic values
syn_value = self.stp(self.g)
syn_value = self.g.value
if self.stp is not None: syn_value = self.stp(syn_value)
if isinstance(self.conn, All2All):
post_vs = self.syn2post_with_all2all(syn_value, self.g_max)
elif isinstance(self.conn, One2One):
Expand Down Expand Up @@ -553,8 +554,8 @@ def reset_state(self, batch_size=None):
self.g = variable(bm.zeros, batch_size, self.pre.num)
self.x = variable(bm.zeros, batch_size, self.pre.num)
self.spike_arrival_time = variable(lambda s: bm.ones(s) * -1e7, batch_size, self.pre.num)
self.stp.reset_state(batch_size)
self.output.reset_state(batch_size)
if self.stp is not None: self.stp.reset_state(batch_size)

def dg(self, g, t, x):
return self.alpha1 * x * (1 - g) - self.beta1 * g
Expand All @@ -574,7 +575,7 @@ def update(self, tdi, pre_spike=None):

# update sub-components
self.output.update(tdi)
self.stp.update(tdi, pre_spike)
if self.stp is not None: self.stp.update(tdi, pre_spike)

# update synapse variables
self.spike_arrival_time.value = bm.where(pre_spike, t, self.spike_arrival_time)
Expand All @@ -584,7 +585,8 @@ def update(self, tdi, pre_spike=None):
self.g.value, self.x.value = self.integral(self.g, self.x, t, T, dt)

# post-synaptic value
syn_value = self.stp(self.g)
syn_value = self.g.value
if self.stp is not None: syn_value = self.stp(syn_value)
if isinstance(self.conn, All2All):
post_vs = self.syn2post_with_all2all(syn_value, self.g_max)
elif isinstance(self.conn, One2One):
Expand Down
16 changes: 4 additions & 12 deletions brainpy/train/back_propagation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -232,20 +232,12 @@ def fit(
print(msg)
t0 = t1

# # testing set
# if test_data is not None:
# test_data_ = self._get_batchable_data(test_data, batch_size, False)
# for x, y in test_data_:
# if reset_state:
# self.target.reset_state(self._get_batch_size(x))
# self.reset_state()
# loss = self.f_loss(shared_args)(x, y)
# all_test_losses.append(loss)

# finally
self._train_losses = bm.asarray(all_train_losses)
self._train_loss_aux = {k: bm.asarray(v) for k, v in all_train_loss_aux.items()}
# self._test_losses = bm.asarray(all_test_losses)
if all_train_loss_aux is None:
self._train_loss_aux = dict()
else:
self._train_loss_aux = {k: bm.asarray(v) for k, v in all_train_loss_aux.items()}
self.progress_bar = true_progress_bar

def _get_batchable_data(self, data, num_batch, shuffle=False):
Expand Down
2 changes: 1 addition & 1 deletion examples/training/SurrogateGrad_lif_fashion_mnist.py
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Original file line number Diff line number Diff line change
Expand Up @@ -197,7 +197,7 @@ def get_mini_batch_results(model, x_data, y_data, batch_size=128, nb_steps=100,
net = SNN(num_in=num_input, num_rec=100, num_out=10)

# load the dataset
root = "some_path/"
root = r"E:\data\fashion-mnist"
train_dataset = bp.datasets.FashionMNIST(root,
train=True,
transform=None,
Expand Down