Add simpler verbose mode to Sequential model
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@ -121,7 +121,8 @@ class Sequential(object):
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np.random.shuffle(index_array)
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batches = make_batches(len(X), batch_size)
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progbar = Progbar(target=len(X))
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if verbose==1:
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progbar = Progbar(target=len(X))
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for batch_index, (batch_start, batch_end) in enumerate(batches):
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if shuffle:
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batch_ids = index_array[batch_start:batch_end]
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@ -138,23 +139,30 @@ class Sequential(object):
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# logging
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if verbose:
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is_last_batch = (batch_index == len(batches) - 1)
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if not is_last_batch or not do_validation:
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if show_accuracy:
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progbar.update(batch_end, [('loss', loss), ('acc.', acc)])
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else:
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progbar.update(batch_end, [('loss', loss)])
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if (not is_last_batch or not do_validation):
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if verbose==1:
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if show_accuracy:
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progbar.update(batch_end, [('loss', loss), ('acc.', acc)])
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else:
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progbar.update(batch_end, [('loss', loss)])
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else:
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if show_accuracy:
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val_loss, val_acc = self.test(X_val, y_val, accuracy=True)
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progbar.update(batch_end, [('loss', loss), ('acc.', acc), ('val. loss', val_loss), ('val. acc.', val_acc)])
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if verbose==1:
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progbar.update(batch_end, [('loss', loss), ('acc.', acc), ('val. loss', val_loss), ('val. acc.', val_acc)])
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if verbose==2:
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print("loss: %.4f - acc.: %.4f - val. loss: %.4f - val. acc.: %.4f" % (loss, acc, val_loss, val_acc))
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else:
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val_loss = self.test(X_val, y_val, accuracy=False)
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progbar.update(batch_end, [('loss', loss), ('val. loss', val_loss)])
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if verbose==1:
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progbar.update(batch_end, [('loss', loss), ('val. loss', val_loss)])
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if verbose==2:
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print("loss: %.4f - acc.: %.4f" % (loss, acc))
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def predict_proba(self, X, batch_size=128, verbose=1):
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batches = make_batches(len(X), batch_size)
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if verbose:
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if verbose==1:
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progbar = Progbar(target=len(X))
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for batch_index, (batch_start, batch_end) in enumerate(batches):
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X_batch = X[batch_start:batch_end]
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@ -165,8 +173,9 @@ class Sequential(object):
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preds = np.zeros(shape)
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preds[batch_start:batch_end] = batch_preds
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if verbose:
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if verbose==1:
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progbar.update(batch_end)
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return preds
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@ -199,10 +208,16 @@ class Sequential(object):
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tot_score += loss
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if verbose:
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if show_accuracy:
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progbar.update(batch_end, [('loss', loss), ('acc.', acc)])
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else:
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progbar.update(batch_end, [('loss', loss)])
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if verbose==1:
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if show_accuracy:
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progbar.update(batch_end, [('loss', loss), ('acc.', acc)])
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else:
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progbar.update(batch_end, [('loss', loss)])
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if batch_index == len(batches) and verbose==2:
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if show_accuracy:
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print("loss: %.4f - acc.: %.4f" % (loss, acc))
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else:
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print("loss: %.4f")
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if show_accuracy:
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return tot_score/len(batches), tot_acc/len(batches)
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