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[英]Model fit / TypeError: 'NoneType' object is not callable
[英]model fit/ TypeError: 'NoneType' object is not callable
您好,我正在嘗試根據以下代碼運行 model fit,但不知何故它一直在說
TypeError: 'NoneType' object 不可調用。 不確定我做錯了哪一部分。 這是
我的優化訓練過程的一部分。 我在這里迷路了...運行這樣的 model.fit 是否有最低要求?
請在這件事上給予我幫助!
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import datasets
(train_x, train_y), (test_x, test_y) = datasets.mnist.load_data()
inputs = layers.Input((28, 28, 1))
net = layers.Conv2D(32, (3, 3), padding ='SAME')(inputs)
net = layers.Activation('relu')(net)
net = layers.Conv2D(32, (3, 3), padding ='SAME')(net)
net = layers.Activation('relu')(net)
net = layers.MaxPooling2D(pool_size=(2, 2))(net)
net = layers.Dropout(0.25)(net)
net = layers.Conv2D(64, (3, 3), padding ='SAME')(net)
net = layers.Activation('relu')(net)
net = layers.Conv2D(64, (3, 3), padding ='SAME')(net)
net = layers.Activation('relu')(net)
net = layers.MaxPooling2D(pool_size=(2, 2))(net)
net = layers.Dropout(0.25)(net)
net = layers.Flatten()(net)
net = layers.Dense(512)(net)
net = layers.Activation('relu')(net)
net = layers.Dropout(0.5)(net)
net = layers.Dense(10)(net)
net = layers.Activation('softmax')(net)
model = tf.keras.Model(inputs=inputs, outputs=net, name='Basic_CNN')
loss_fun = tf.keras.losses.sparse_categorical_crossentropy
metrics = tf.keras.metrics.Accuracy()
optm = tf.keras.optimizers.Adam()
model.compile(optimizer=tf.keras.optimizers.Adam(),
loss='sparse_categorical_crossentropy',
metrics=[tf.keras.metrics.Accuracy()])
train_x.shape, train_y.shape
test_x.shape, test_y.shape
import numpy as np
np.expand_dims(train_x, -1).shape
tf.expand_dims(train_x, -1).shape
train_x = train_x[..., tf.newaxis]
test_x = test_x[..., tf.newaxis]
train_x.shape
np.min(train_x), np.max(train_x)
train_x = train_x / 255.
test_x = test_x / 255.
np.min(train_x), np.max(train_x)
num_epochs = 1
batch_size = 32
model.fit(train_x, train_y,
batch_size=batch_size,
shuffle=True,
epochs=num_epochs)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-20-870033ef5c40> in <module>
2 batch_size=batch_size,
3 shuffle=True,
----> 4 epochs=num_epochs)
~/opt/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
~/opt/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1096 batch_size=batch_size):
1097 callbacks.on_train_batch_begin(step)
-> 1098 tmp_logs = train_function(iterator)
1099 if data_handler.should_sync:
1100 context.async_wait()
~/opt/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args, **kwds)
781
782 new_tracing_count = self._get_tracing_count()
~/opt/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
805 # In this case we have created variables on the first call, so we run the
806 # defunned version which is guaranteed to never create variables.
--> 807 return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable
808 elif self._stateful_fn is not None:
809 # Release the lock early so that multiple threads can perform the call
TypeError: 'NoneType' object is not callable
你必須做兩件事。
首先,您必須將 loss 更改為: categorical_crossentropy
。
其次,你需要你的train_y
和test_y
必須是單熱編碼的。 這意味着它們必須具有維度(number_of_samples, 10)
,其中10
表示類的數量。 在model.compile():
之后添加model.compile():
num_classes = 10 #number of classes, here is 10 (0,1,...,9)
train_y = keras.utils.to_categorical(train_y, num_classes)
test_y = keras.utils.to_categorical(test_y, num_classes)
最后,讓我說你應該改變時代數和批量大小以獲得更好的結果。 例如epochs count = 12
和batch size = 128
當我按名稱引用損失和度量函數時,我遇到了這個錯誤。 在我用對象替換名稱后錯誤消失了。 在您的情況下,我建議添加導入語句並更改 model 編譯參數,即:
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import SparseCategoricalCrossentropy
from tensorflow.keras.metrics import Accuracy
...
model.compile(optimizer=Adam(),
loss=SparseCategoricalCrossentropy(from_logits=True),
metrics=[Accuracy()])
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