【发布时间】：2021-07-06 04:56:10
【问题描述】：
我的机器学习代码有问题
这是模型：
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=input_shape),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(64, 3, activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(128, 3, activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(512, 3, activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(1, activation='softmax')])
model.summary()
这是我的 model.summary() 结果：
Model: "sequential_32"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_128 (Conv2D) (None, 148, 148, 32) 896
_________________________________________________________________
max_pooling2d_128 (MaxPoolin (None, 74, 74, 32) 0
_________________________________________________________________
conv2d_129 (Conv2D) (None, 72, 72, 64) 18496
_________________________________________________________________
max_pooling2d_129 (MaxPoolin (None, 36, 36, 64) 0
_________________________________________________________________
conv2d_130 (Conv2D) (None, 34, 34, 128) 73856
_________________________________________________________________
max_pooling2d_130 (MaxPoolin (None, 17, 17, 128) 0
_________________________________________________________________
conv2d_131 (Conv2D) (None, 15, 15, 512) 590336
_________________________________________________________________
max_pooling2d_131 (MaxPoolin (None, 7, 7, 512) 0
_________________________________________________________________
flatten_32 (Flatten) (None, 25088) 0
_________________________________________________________________
dense_79 (Dense) (None, 128) 3211392
_________________________________________________________________
dense_80 (Dense) (None, 1) 129
=================================================================
Total params: 3,895,105
Trainable params: 3,895,105
Non-trainable params: 0
这是我使用的编译设置：
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
这是适合的模型代码：
EPOCH = 100
history = model.fit(train_data,
steps_per_epoch=len(train_generator),
epochs=EPOCH,
validation_data=val_data,
validation_steps=len(val_generator),
shuffle=True,
verbose = 1)
对于我创建的 train_data 使用 tensorflow tf.data，因为我认为它与 tf.keras 更兼容。这是 tf.data 生成器功能代码：
def tf_data_generator(generator, input_shape):
num_class = generator.num_classes
tf_generator = tf.data.Dataset.from_generator(
lambda: generator,
output_types=(tf.float32, tf.float32),
output_shapes=([None
, input_shape[0]
, input_shape[1]
, input_shape[2]]
,[None, num_class])
)
return tf_generator
train_data = tf_data_generator(train_generator, input_shape)
val_data = tf_data_generator(val_generator, input_shape)
实际上，我从 medium.com 作为源获得了该功能。但是我在尝试训练我的机器学习代码时遇到了错误，有人可以帮我解决错误吗，这是错误消息：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-16-448faadd058c> in <module>()
6 validation_steps=len(val_generator),
7 shuffle=True,
----> 8 verbose = 1)
9 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
984 except Exception as e: # pylint:disable=broad-except
985 if hasattr(e, "ag_error_metadata"):
--> 986 raise e.ag_error_metadata.to_exception(e)
987 else:
988 raise
ValueError: in user code:
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:855 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:845 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1285 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2833 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3608 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:838 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:797 train_step
y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/compile_utils.py:204 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:155 __call__
losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:259 call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:1644 categorical_crossentropy
y_true, y_pred, from_logits=from_logits)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py:4862 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:1161 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, 3) and (None, 1) are incompatible
对不起，如果我的问题令人困惑，我还是机器学习领域的新手。谢谢你帮助我
【问题讨论】：
标签： python tensorflow machine-learning keras conv-neural-network