Python TensorFlow2-模型子类化值错误
我正在尝试使用TensorFlow 2的模型子分类创建LeNet-300-100密集神经网络。我拥有的代码如下:Python TensorFlow2-模型子类化值错误,python,tensorflow,neural-network,Python,Tensorflow,Neural Network,我正在尝试使用TensorFlow 2的模型子分类创建LeNet-300-100密集神经网络。我拥有的代码如下: batch_size = 32 num_epochs = 20 # Load MNIST dataset- (X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data() X_train = X_train.astype('float32') / 255.0 X_test = X_test.a
batch_size = 32
num_epochs = 20
# Load MNIST dataset-
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()
X_train = X_train.astype('float32') / 255.0
X_test = X_test.astype('float32') / 255.0
# Convert class vectors/target to binary class matrices or one-hot encoded values-
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
X_train.shape, y_train.shape
# ((60000, 28, 28), (60000, 10))
X_test.shape, y_test.shape
# ((10000, 28, 28), (10000, 10))
class LeNet300(Model):
def __init__(self, **kwargs):
super(LeNet300, self).__init__(**kwargs)
self.flatten = Flatten()
self.dense1 = Dense(units = 300, activation = 'relu')
self.dense2 = Dense(units = 100, activation = 'relu')
self.op = Dense(units = 10, activation = 'softmax')
def call(self, inputs):
x = self.flatten(inputs)
x = self.dense1(x)
x = self.dense2(x)
return self.op(x)
# Instantiate an object using LeNet-300-100 dense model-
model = LeNet300()
# Compile the defined model-
model.compile(
optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy']
)
# Define early stopping callback-
early_stopping_callback = tf.keras.callbacks.EarlyStopping(
monitor = 'val_loss', min_delta = 0.001,
patience = 3)
# Train defined and compiled model-
history = model.fit(
x = X_train, y = y_train,
batch_size = batch_size, shuffle = True,
epochs = num_epochs,
callbacks = [early_stopping_callback],
validation_data = (X_test, y_test)
)
调用“model.fit()”时,会出现以下错误:
ValueError:形状不匹配:标签的形状(已接收(320,))
除最后一个尺寸外,应等于罗格特的形状
(已收到(32,10))
怎么了
这是因为第一个密集层的输入应该是平坦的。MNIST数据每个数字有28x28个网格/图像。此28x28数据应展平为784个输入数字 因此,就在第一个
密集(…)
层插入flatte()
keras层之前,即执行flatte()(输入)
请参阅展平层的参考。损耗
稀疏分类交叉熵
不需要一个热编码来计算损耗。在文件中,他们提到
当有两个或多个标签类时,使用此交叉熵损失函数。我们希望标签以整数形式提供。如果要使用一个热表示提供标签,请使用CategoricalCrossentropy loss。对于y#u pred,每个功能应有#类浮点值,对于y#u true,每个功能应有一个浮点值
因此,您将得到错误。如果观察stacktrace,损失函数中会出现错误
/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/keras/losses.py:1569 sparse_categorical_crossentropy
y_true, y_pred, from_logits=from_logits, axis=axis)
/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/keras/backend.py:4941 sparse_categorical_crossentropy
labels=target, logits=output)
/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py:4241 sparse_softmax_cross_entropy_with_logits_v2
labels=labels, logits=logits, name=name)
/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py:4156 sparse_softmax_cross_entropy_with_logits
logits.get_shape()))
ValueError: Shape mismatch: The shape of labels (received (320,)) should equal the shape of logits except for the last dimension (received (32, 10)).
我建议使用
CategoricalCrossentropy
数据应在密集层之前展平,请参见。我已按照建议编辑了我的代码(编辑问题以添加编辑)。我得到的新错误是:ValueError:Shape mismatch:标签的形状(received(320,))应该与logit的形状相等,除了最后一个维度(received(32,10))。