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diff --git a/experiments/omniglot_binary_classif.py b/experiments/omniglot_binary_classif.py
index fc6a575307a4f57b457dd7cdce08a0211d944ec2..3a913f61d01fbef10228dd77494bdc632a79353e 100644
--- a/experiments/omniglot_binary_classif.py
+++ b/experiments/omniglot_binary_classif.py
@@ -85,4 +85,4 @@ print(name_model)
path_model_checkpoint = 'trained_models/Omniglot_classif/Mixt_models/maxpooling/'
path_save_plot = 'results/Omniglot_results/plot_acc_loss/Omniglot_classif/'
-run(model, path_model_checkpoint, path_save_plot, name_model, train_loader, valid_loader, epochs, lr, momentum, criterion, log_interval)
\ No newline at end of file
+run(model, path_model_checkpoint, path_save_plot, name_model, train_loader, valid_loader, epochs, lr, momentum, criterion, log_interval)
diff --git a/results/Omniglot_results/plot_acc_loss/Omniglot_classif/MNIST_Stochastic_ST_maxpooling_mixt_acc.png b/results/Omniglot_results/plot_acc_loss/Omniglot_classif/MNIST_Stochastic_ST_maxpooling_mixt_acc.png
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diff --git a/utils/__pycache__/models.cpython-37.pyc b/utils/__pycache__/models.cpython-37.pyc
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diff --git a/utils/__pycache__/training.cpython-37.pyc b/utils/__pycache__/training.cpython-37.pyc
index 204c502a497bac6fb09886f199654d0d20b93035..4a410d8c805c5aa41e5ee86e3798635b78dfa180 100644
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diff --git a/utils/models.py b/utils/models.py
index 11ffc5a911ea6a4ae952fc5f76117fe0ec0e2180..12fc579dde615d32782e50f01363383c60ffbcd8 100644
--- a/utils/models.py
+++ b/utils/models.py
@@ -137,10 +137,10 @@ def get_my_model_Omniglot(binary, maxpooling=True, mixt=False, stochastic=True,
elif mixt:
if stochastic:
mode = 'Stochastic'
- names_model = 'MNIST_Stochastic'
+ names_model = 'Omniglot_Stochastic'
else:
mode = 'Deterministic'
- names_model = 'MNIST_Deterministic'
+ names_model = 'Omniglot_Deterministic'
if reinforce:
estimator = 'REINFORCE'
names_model += '_REINFORCE'
@@ -153,10 +153,10 @@ def get_my_model_Omniglot(binary, maxpooling=True, mixt=False, stochastic=True,
model = MixtNetOmniglotClassification(maxpooling, mode=mode, estimator=estimator)
else:
- model = NoBinaryNetOmniglotClassification(maxpooling)
names_model = 'Omniglot_classif_NonBinaryNet'
- mode = None
- estimator = None
+ if maxpooling:
+ names_model += '_maxpooling'
+ model = NoBinaryNetOmniglotClassification(maxpooling)
return model, names_model
@@ -383,7 +383,10 @@ class NoBinaryNetOmniglotClassification(Net):
self.batchNorm5 = nn.BatchNorm2d(512)
self.act_layer5 = nn.ReLU()
- self.fc1 = nn.Linear(4 * 4 * 512, 4096)
+ if self.maxpooling:
+ self.fc1 = nn.Linear(3 * 3 * 512, 4096)
+ else:
+ self.fc1 = nn.Linear(4 * 4 * 512, 4096)
self.act_fc1 = nn.ReLU()
self.dropout1 = nn.Dropout(0.5)
self.fc2 = nn.Linear(4096, 1623)
@@ -485,7 +488,11 @@ class BinaryNetOmniglotClassification(Net):
self.act_layer5 = nn.ReLU()
- self.fc1 = nn.Linear(4 * 4 * 512, 4096)
+ if self.maxpooling:
+ self.fc1 = nn.Linear(3 * 3 * 512, 4096)
+ else:
+ self.fc1 = nn.Linear(4 * 4 * 512, 4096)
+
self.act_fc1 = nn.ReLU()
self.dropout1 = nn.Dropout(0.5)
self.fc2 = nn.Linear(4096, 1623)
@@ -609,7 +616,11 @@ class MixtNetOmniglotClassification(Net):
self.act_layer4_binary = StochasticBinaryActivation(estimator=estimator)
self.act_layer5_binary = StochasticBinaryActivation(estimator=estimator)
- self.fc1 = nn.Linear(4*4*256*2, 4096)
+ if self.maxpooling:
+ self.fc1 = nn.Linear(3*3*256*2, 4096)
+ else:
+ self.fc1 = nn.Linear(4*4*256*2, 4096)
+
self.act_fc1 = nn.ReLU()
self.dropout1 = nn.Dropout(0.5)
self.fc2 = nn.Linear(4096, 1623)
@@ -621,34 +632,35 @@ class MixtNetOmniglotClassification(Net):
if self.maxpooling:
# For binary:
- x_layer1_binary = self.act_layer1_binary(((self.maxpool1_binary(self.batchnorm1_binary(self.layer1_binary(x)))), slope))
- x_layer2_binary = self.act_layer2_binary(((self.maxpool2_binary(self.batchnorm2_binary(self.layer2_binary(x_layer1_binary)))), slope))
- x_layer3_binary = self.act_layer3_binary(((self.maxpool3_binary(self.batchnorm3_binary(self.layer3_binary(x_layer2_binary)))), slope))
- x_layer4_binary = self.act_layer4_binary(((self.maxpool4_binary(self.batchnorm4_binary(self.layer4_binary(x_layer3_binary)))), slope))
- x_layer5_binary = self.act_layer5_binary(((self.maxpool5_binary(self.batchnorm5_binary(self.layer5_binary(x_layer4_binary)))), slope))
+ x_layer1_binary = self.act_layer1_binary(((self.maxPool1_binary(self.batchNorm1_binary(self.layer1_binary(x)))), slope))
+ x_layer2_binary = self.act_layer2_binary(((self.maxPool2_binary(self.batchNorm2_binary(self.layer2_binary(x_layer1_binary)))), slope))
+ x_layer3_binary = self.act_layer3_binary(((self.maxPool3_binary(self.batchNorm3_binary(self.layer3_binary(x_layer2_binary)))), slope))
+ x_layer4_binary = self.act_layer4_binary(((self.maxPool4_binary(self.batchNorm4_binary(self.layer4_binary(x_layer3_binary)))), slope))
+ x_layer5_binary = self.act_layer5_binary(((self.maxPool5_binary(self.batchNorm5_binary(self.layer5_binary(x_layer4_binary)))), slope))
# For No binary:
- x_layer1_no_binary = self.act_layer1_no_binary(self.maxpool1_no_binary(self.batchnorm1_no_binary(self.layer1_no_binary(x) * slope)))
- x_layer2_no_binary = self.act_layer2_no_binary(self.maxpool2_no_binary(self.batchnorm2_no_binary(self.layer2_no_binary(x_layer1_no_binary) * slope)))
- x_layer3_no_binary = self.act_layer3_no_binary(self.maxpool3_no_binary(self.batchnorm3_no_binary(self.layer3_no_binary(x_layer2_no_binary) * slope)))
- x_layer4_no_binary = self.act_layer4_no_binary(self.maxpool4_no_binary(self.batchnorm4_no_binary(self.layer4_no_binary(x_layer3_no_binary) * slope)))
- x_layer5_no_binary = self.act_layer5_no_binary(self.maxpool5_no_binary(self.batchnorm5_no_binary(self.layer5_no_binary(x_layer4_no_binary) * slope)))
+ x_layer1_no_binary = self.act_layer1_no_binary(self.maxPool1_no_binary(self.batchNorm1_no_binary(self.layer1_no_binary(x) * slope)))
+ x_layer2_no_binary = self.act_layer2_no_binary(self.maxPool2_no_binary(self.batchNorm2_no_binary(self.layer2_no_binary(x_layer1_no_binary) * slope)))
+ x_layer3_no_binary = self.act_layer3_no_binary(self.maxPool3_no_binary(self.batchNorm3_no_binary(self.layer3_no_binary(x_layer2_no_binary) * slope)))
+ x_layer4_no_binary = self.act_layer4_no_binary(self.maxPool4_no_binary(self.batchNorm4_no_binary(self.layer4_no_binary(x_layer3_no_binary) * slope)))
+ x_layer5_no_binary = self.act_layer5_no_binary(self.maxPool5_no_binary(self.batchNorm5_no_binary(self.layer5_no_binary(x_layer4_no_binary) * slope)))
else:
# For binary:
- x_layer1_binary = self.act_layer1_binary(((self.batchnorm1_binary(self.layer1_binary(x))), slope))
- x_layer2_binary = self.act_layer2_binary(((self.batchnorm2_binary(self.layer2_binary(x_layer1_binary))), slope))
- x_layer3_binary = self.act_layer3_binary(((self.batchnorm3_binary(self.layer3_binary(x_layer2_binary))), slope))
- x_layer4_binary = self.act_layer4_binary(((self.batchnorm4_binary(self.layer4_binary(x_layer3_binary))), slope))
- x_layer5_binary = self.act_layer5_binary(((self.batchnorm5_binary(self.layer5_binary(x_layer4_binary))), slope))
+ x_layer1_binary = self.act_layer1_binary(((self.batchNorm1_binary(self.layer1_binary(x))), slope))
+
+ x_layer2_binary = self.act_layer2_binary(((self.batchNorm2_binary(self.layer2_binary(x_layer1_binary))), slope))
+ x_layer3_binary = self.act_layer3_binary(((self.batchNorm3_binary(self.layer3_binary(x_layer2_binary))), slope))
+ x_layer4_binary = self.act_layer4_binary(((self.batchNorm4_binary(self.layer4_binary(x_layer3_binary))), slope))
+ x_layer5_binary = self.act_layer5_binary(((self.batchNorm5_binary(self.layer5_binary(x_layer4_binary))), slope))
# For no binary:
- x_layer1_no_binary = self.act_layer1_no_binary(self.batchnorm1_no_binary(self.layer1_no_binary(x) * slope))
- x_layer2_no_binary = self.act_layer2_no_binary(self.batchnorm2_no_binary(self.layer2_no_binary(x_layer1_no_binary) * slope))
- x_layer3_no_binary = self.act_layer3_no_binary(self.batchnorm3_no_binary(self.layer3_no_binary(x_layer2_no_binary) * slope))
- x_layer4_no_binary = self.act_layer4_no_binary(self.batchnorm4_no_binary(self.layer4_no_binary(x_layer3_no_binary) * slope))
- x_layer5_no_binary = self.act_layer5_no_binary(self.batchnorm5_no_binary(self.layer5_no_binary(x_layer4_no_binary) * slope))
+ x_layer1_no_binary = self.act_layer1_no_binary(self.batchNorm1_no_binary(self.layer1_no_binary(x) * slope))
+ x_layer2_no_binary = self.act_layer2_no_binary(self.batchNorm2_no_binary(self.layer2_no_binary(x_layer1_no_binary) * slope))
+ x_layer3_no_binary = self.act_layer3_no_binary(self.batchNorm3_no_binary(self.layer3_no_binary(x_layer2_no_binary) * slope))
+ x_layer4_no_binary = self.act_layer4_no_binary(self.batchNorm4_no_binary(self.layer4_no_binary(x_layer3_no_binary) * slope))
+ x_layer5_no_binary = self.act_layer5_no_binary(self.batchNorm5_no_binary(self.layer5_no_binary(x_layer4_no_binary) * slope))
- x_layer5_binary = x_layer2_binary.view(x_layer5_binary.size(0), -1)
- x_layer5_no_binary = x_layer2_no_binary.view(x_layer5_no_binary.size(0), -1)
+ x_layer5_binary = x_layer5_binary.view(x_layer5_binary.size(0), -1)
+ x_layer5_no_binary = x_layer5_no_binary.view(x_layer5_no_binary.size(0), -1)
x_concatenate = torch.cat((x_layer5_binary, x_layer5_no_binary), 1)
x_fc1 = self.dropout1(self.act_fc1(self.fc1(x_concatenate)))
x_fc2 = self.fc2(x_fc1)