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num_epoch = 50 | |
print_freq = 10 | |
class Histories(Callback): | |
def on_train_begin(self, logs={}): | |
self.epoch = [] | |
self.history = {} | |
self.history["batch"] = [] | |
def on_train_end(self, logs={}): | |
return | |
def on_epoch_begin(self, epoch, logs={}): | |
self.epoch.append(epoch) | |
self.batch = [] | |
self.history['batch'] = {} | |
self.history['batch']['acc'] = [0] * self.params['steps'] | |
self.history['batch']['loss'] = [0] * self.params['steps'] | |
def on_epoch_end(self, epoch, logs={}): | |
for k, v in logs.items(): | |
self.history.setdefault(k, []).append(v) | |
def on_batch_begin(self, batch, logs={}): | |
self.batch.append(batch) | |
def on_batch_end(self, batch, logs={}): | |
self.history['batch']['acc'][batch] = logs['acc'] | |
self.history['batch']['loss'][batch] = logs['loss'] | |
if (((batch+1) % print_freq) == 0) or (batch == self.params['steps']-1): | |
num_batch = self.params['steps'] | |
batch_loss = self.history['batch']['loss'][batch] | |
batch_acc = self.history['batch']['acc'][batch] | |
train_loss = mean(self.history['batch']['loss'][:batch+1]) | |
train_acc = mean(self.history['batch']['acc'][:batch+1]) | |
print('Ep %2d | Batch (%3d / %3d) | Trn [BLoss %.5f | BAcc %.5f | Loss %.5f | Acc %.5f]' | |
% (epoch+1, batch+1, num_batch, batch_loss, batch_acc, train_loss, train_acc), end='\r') | |
train_gen = genBatch(X_train, Y_train, batch_size, num_batches) | |
val_gen = genBatch(X_test, Y_test, batch_size_val, num_batches_val) | |
model.fit_generator(generator=train_gen, | |
steps_per_epoch=num_batches, | |
initial_epoch=epoch, | |
epochs=epoch+1, | |
validation_data=val_gen, | |
validation_steps=num_batches_val, | |
callbacks=[learning_rate_scheduler, h], | |
verbose=0) |
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