class LossHistory(Callback):
def __init__(self, X_train, y_train, layer_index):
super(Callback, self).__init__()
self.layer_index = layer_index
if X_train.shape[0] >= 1000:
mask = np.random.choice(X_train.shape[0], 1000)
self.X_train_subset = X_train[mask]
self.y_train_subset = y_train[mask]
else:
self.X_train_subset = X_train
self.y_train_subset = y_train
def on_train_begin(self, logs={}):
self.train_batch_loss = []
self.train_acc = []
self.val_acc = []
self.relu_out = []
def on_batch_end(self, batch, logs={}):
self.train_batch_loss.append(logs.get('loss'))
def on_epoch_end(self, epoch, logs={}):
self.relu_out.append(self.get_layer_out())
val_epoch_acc = logs.get('val_acc')
self.val_acc.append(val_epoch_acc)
train_epoch_acc = self.model.evaluate(self.X_train_subset, self.y_train_subset,
show_accuracy=True, verbose=0)[1]
self.train_acc.append(train_epoch_acc)
print('(train accuracy, val accuracy): (%.4f, %.4f)' % (train_epoch_acc, val_epoch_acc))
def get_layer_out(self):
layer_index = self.layer_index
get_activation = theano.function([self.model.layers[0].input],
self.model.layers[layer_index].get_output(train=False), allow_input_downcast=True)
return get_activation(self.X_train_subset)
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class LossHistory(Callback): | |
def __init__(self, X_train, y_train, layer_index): | |
super(Callback, self).__init__() | |
self.layer_index = layer_index | |
self.previous_model_params_ = None | |
if X_train.shape[0] >= 1000: | |
mask = np.random.choice(X_train.shape[0], 1000) | |
self.X_train_subset = X_train[mask] | |
self.y_train_subset = y_train[mask] | |
else: | |
self.X_train_subset = X_train | |
self.y_train_subset = y_train | |
def on_train_begin(self, logs={}): | |
#self.model_params would be ['layer1_W', 'layer2_b', 'layer2_W', 'layer2_b',...] | |
self.train_batch_loss = [] | |
self.train_acc = [] | |
self.val_acc = [] | |
self.relu_out = [] | |
self.model_params = [] | |
self.gradients = [] | |
def on_batch_end(self, batch, logs={}): | |
self.train_batch_loss.append(logs.get('loss')) | |
def on_epoch_end(self, epoch, logs={}): | |
current_model_params = self.get_model_params() | |
if not self.previous_model_params_: | |
self.previous_model_params_ = current_model_params | |
else: | |
gradients = [(param - prev_param) for (param, prev_param) in zip(current_model_params, previous_model_params_)] | |
self.gradients.append(gradients) | |
self.previous_model_params_ = current_model_params | |
self.model_params.append(current_model_params) | |
self.relu_out.append(self.get_layer_out()) | |
val_epoch_acc = logs.get('val_acc') | |
self.val_acc.append(val_epoch_acc) | |
train_epoch_acc = self.model.evaluate(self.X_train_subset, self.y_train_subset, | |
show_accuracy=True, verbose=0)[1] | |
self.train_acc.append(train_epoch_acc) | |
print('(train accuracy, val accuracy): (%.4f, %.4f)' % (train_epoch_acc, val_epoch_acc)) | |
def get_layer_out(self): | |
layer_index = self.layer_index | |
get_activation = theano.function([self.model.layers[0].input], | |
self.model.layers[layer_index].get_output(train=False), allow_input_downcast=True) | |
return get_activation(self.X_train_subset) | |
def get_model_params(self): | |
return [param.get_value() for param in self.model.params] |
Author
jiumem
commented
Sep 23, 2015
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