Я хочу для обучения CNN с ранней остановкой и хочу использовать f1-metri c в качестве критерия остановки. Когда я компилирую код для модели CNN, я получаю a TypeError как сообщение об ошибке. Я до сих пор использую Tensorflow 1.4, поэтому хотел бы избежать обновления до 2.0, поскольку я имею в виду, что мой предыдущий код больше не работает.
Сообщение об ошибке выглядит следующим образом:
TypeError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in _num_samples(x)
158 try:
--> 159 return len(x)
160 except TypeError:
14 frames
/usr/local/lib/python3.6/dist-
packages/tensorflow_core/python/framework/ops.py in __len__(self)
740 "Please call `x.shape` rather than `len(x)` for "
--> 741 "shape information.".format(self.name))
742
TypeError: len is not well defined for symbolic Tensors. (dense_16_target:0) Please call `x.shape` rather than `len(x)` for shape information.
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-44-cd3da16e057c> in <module>()
----> 1 model = model_cnn(False,False, False,True,6, 0.2, 0.5)
2 X_train, X_val, y_train, y_val = split_data(X_train, y_train,1)
3 cnn, ep = train_model_es(model, X_train, y_train, X_val, y_val, X_test, y_test, 50, 500,1)
<ipython-input-42-d275d9c69c03> in model_cnn(spat, extra_pool, avg_pool, cw, numb_conv, drop_conv, drop_dense)
36 if cw == True:
37 print("sparse categorical crossentropy")
---> 38 model.compile(loss="sparse_categorical_crossentropy", optimizer=Adam(), metrics=['accuracy', f1_metric])
39 #model.compile(loss="sparse_categorical_crossentropy", optimizer=Adam(), metrics=['accuracy'])
40 print("nothing")
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, **kwargs)
452 output_metrics = nested_metrics[i]
453 output_weighted_metrics = nested_weighted_metrics[i]
--> 454 handle_metrics(output_metrics)
455 handle_metrics(output_weighted_metrics, weights=weights)
456
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in handle_metrics(metrics, weights)
421 metric_result = weighted_metric_fn(y_true, y_pred,
422 weights=weights,
--> 423 mask=masks[i])
424
425 # Append to self.metrics_names, self.metric_tensors,
/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py in weighted(y_true, y_pred, weights, mask)
426 """
427 # score_array has ndim >= 2
--> 428 score_array = fn(y_true, y_pred)
429 if mask is not None:
430 # Cast the mask to floatX to avoid float64 upcasting in Theano
<ipython-input-9-b21dc3bd89a6> in f1_metric(y_test, y_pred)
1 def f1_metric(y_test, y_pred):
----> 2 f1 = f1_score(y_test, y_pred, average='macro')
3 return f1
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in f1_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)
1097 pos_label=pos_label, average=average,
1098 sample_weight=sample_weight,
-> 1099 zero_division=zero_division)
1100
1101
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)
1224 warn_for=('f-score',),
1225 sample_weight=sample_weight,
-> 1226 zero_division=zero_division)
1227 return f
1228
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)
1482 raise ValueError("beta should be >=0 in the F-beta score")
1483 labels = _check_set_wise_labels(y_true, y_pred, average, labels,
-> 1484 pos_label)
1485
1486 # Calculate tp_sum, pred_sum, true_sum ###
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
1299 str(average_options))
1300
-> 1301 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
1302 present_labels = unique_labels(y_true, y_pred)
1303 if average == 'binary':
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in _check_targets(y_true, y_pred)
78 y_pred : array or indicator matrix
79 """
---> 80 check_consistent_length(y_true, y_pred)
81 type_true = type_of_target(y_true)
82 type_pred = type_of_target(y_pred)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in check_consistent_length(*arrays)
206 """
207
--> 208 lengths = [_num_samples(X) for X in arrays if X is not None]
209 uniques = np.unique(lengths)
210 if len(uniques) > 1:
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in <listcomp>(.0)
206 """
207
--> 208 lengths = [_num_samples(X) for X in arrays if X is not None]
209 uniques = np.unique(lengths)
210 if len(uniques) > 1:
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in _num_samples(x)
159 return len(x)
160 except TypeError:
--> 161 raise TypeError(message)
162
163
TypeError: Expected sequence or array-like, got <class 'tensorflow.python.framework.ops.Tensor'>
А вот соответствующий код:
def f1_metric(y_test, y_pred):
f1 = f1_score(y_test, y_pred, average='macro')
return f1
def train_model_es(model, X, y, X_val, y_val, X_test, y_test):
es = EarlyStopping(monitor='f1_metric', mode='max', patience=20, restore_best_weights=True)
y = np.argmax(y, axis=1)
y_val = np.argmax(y_val, axis=1)
y_test = np.argmax(y_test, axis=1)
class_weights = class_weight.compute_class_weight('balanced', np.unique(y), y)
class_weights = dict(enumerate(class_weights))
history = model.fit(X, y, class_weight=class_weights, batch_size=32,epochs=20, verbose=1,
validation_data=(X_val, y_val), callbacks=[es])
def model_cnn():
model = Sequential()
model.add(Conv2D(32, kernel_size=(3,3), input_shape=(28,28,1),padding='same'))
model.add(BatchNormalization())
model.add(ELU())
model.add(Conv2D(32, kernel_size=(3,3), padding='same'))
model.add(BatchNormalization())
model.add(ELU())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(256))
model.add(BatchNormalization())
model.add(ELU())
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(loss="sparse_categorical_crossentroy",optimizer=Adam(),metrics=["accuracy",f1_metric])
return model
У кого-нибудь есть совет, как исправить это сообщение об ошибке.
Большое спасибо за каждую подсказку