Python Tensorflow InvalidArgumeInterror：在自定义丢失函数之后、纪元结束之前的不兼容形状
我从早上开始调试这个问题，一直没有取得任何进展。我正在尝试训练一个批量大于1的模型。该代码适用于batch_size=1，但不适用于任何较大的数字。如果可能的话，请帮忙 自定义丢失函数的代码为（所有打印语句都是在调试过程中添加的）： 这是我用来初始化模型的Python Tensorflow InvalidArgumeInterror：在自定义丢失函数之后、纪元结束之前的不兼容形状,python,tensorflow,machine-learning,loss-function,Python,Tensorflow,Machine Learning,Loss Function,我从早上开始调试这个问题，一直没有取得任何进展。我正在尝试训练一个批量大于1的模型。该代码适用于batch_size=1，但不适用于任何较大的数字。如果可能的话，请帮忙 自定义丢失函数的代码为（所有打印语句都是在调试过程中添加的）： 这是我用来初始化模型的 model = Model(inputs = inputs, outputs = mobilenet_output, name="main_model") model.compile(optimizer = 'adam'
model = Model(inputs = inputs, outputs = mobilenet_output, name="main_model")
model.compile(optimizer = 'adam'
, loss =mobilenet_loss
, metrics = [ 'accuracy']
)
model.fit(x = train_generator, steps_per_epoch = 1, epochs=2, batch_size=batch_size, shuffle=False)
Epoch 1/2
ytrue = [[2]
[2]] [2 1] y_pred shape = [2 31 31 3]
one_class_shape = [2 31 31 1]
y_true_shape - [2 1]
y_true_compare_0 = [2 1]
y_true_compare_1 = [2 1]
y_true_compare_0 [[0]
[0]]
div_equals_0_check shape = 0
div_equals_1_check shape = 1
after y_true_array = [2 31 31 3] TensorShape([2, 31, 31, 3])
error shape = [2 31 31 3]
loss shape = [] -21462.2773
ytrue = [[2]
[2]] [2 1] y_pred shape = [2 31 31 3]
one_class_shape = [2 31 31 1]
y_true_shape - [2 1]
y_true_compare_0 = [2 1]
y_true_compare_1 = [2 1]
y_true_compare_0 [[0]
[0]]
div_equals_0_check shape = 0
div_equals_1_check shape = 1
after y_true_array = [2 31 31 3] TensorShape([2, 31, 31, 3])
error shape = [2 31 31 3]
loss shape = [] -8.79764557e-05
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-39-739652b805f0> in <module>
----> 1 model.fit(x = train_generator, steps_per_epoch = 1, epochs=2, batch_size=batch_size, shuffle=False)
~/opt/anaconda3/envs/bigthinx/lib/python3.7/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)
1098 _r=1):
1099 callbacks.on_train_batch_begin(step)
-> 1100 tmp_logs = self.train_function(iterator)
1101 if data_handler.should_sync:
1102 context.async_wait()
~/opt/anaconda3/envs/bigthinx/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()
~/opt/anaconda3/envs/bigthinx/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
886 # Lifting succeeded, so variables are initialized and we can run the
887 # stateless function.
--> 888 return self._stateless_fn(*args, **kwds)
889 else:
890 _, _, _, filtered_flat_args = \
~/opt/anaconda3/envs/bigthinx/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
2941 filtered_flat_args) = self._maybe_define_function(args, kwargs)
2942 return graph_function._call_flat(
-> 2943 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access
2944
2945 @property
~/opt/anaconda3/envs/bigthinx/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1917 # No tape is watching; skip to running the function.
1918 return self._build_call_outputs(self._inference_function.call(
-> 1919 ctx, args, cancellation_manager=cancellation_manager))
1920 forward_backward = self._select_forward_and_backward_functions(
1921 args,
~/opt/anaconda3/envs/bigthinx/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
558 inputs=args,
559 attrs=attrs,
--> 560 ctx=ctx)
561 else:
562 outputs = execute.execute_with_cancellation(
~/opt/anaconda3/envs/bigthinx/lib/python3.7/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
InvalidArgumentError: Incompatible shapes: [2,1] vs. [2,31,31]
[[node Equal (defined at <ipython-input-39-739652b805f0>:1) ]] [Op:__inference_train_function_26226]
Function call stack:
train_function
找到了我在这里发布的问题的解决方案
Epoch 1/2
ytrue = [[2]
[2]] [2 1] y_pred shape = [2 31 31 3]
one_class_shape = [2 31 31 1]
y_true_shape - [2 1]
y_true_compare_0 = [2 1]
y_true_compare_1 = [2 1]
y_true_compare_0 [[0]
[0]]
div_equals_0_check shape = 0
div_equals_1_check shape = 1
after y_true_array = [2 31 31 3] TensorShape([2, 31, 31, 3])
error shape = [2 31 31 3]
loss shape = [] -21462.2773
ytrue = [[2]
[2]] [2 1] y_pred shape = [2 31 31 3]
one_class_shape = [2 31 31 1]
y_true_shape - [2 1]
y_true_compare_0 = [2 1]
y_true_compare_1 = [2 1]
y_true_compare_0 [[0]
[0]]
div_equals_0_check shape = 0
div_equals_1_check shape = 1
after y_true_array = [2 31 31 3] TensorShape([2, 31, 31, 3])
error shape = [2 31 31 3]
loss shape = [] -8.79764557e-05
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-39-739652b805f0> in <module>
----> 1 model.fit(x = train_generator, steps_per_epoch = 1, epochs=2, batch_size=batch_size, shuffle=False)
~/opt/anaconda3/envs/bigthinx/lib/python3.7/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)
1098 _r=1):
1099 callbacks.on_train_batch_begin(step)
-> 1100 tmp_logs = self.train_function(iterator)
1101 if data_handler.should_sync:
1102 context.async_wait()
~/opt/anaconda3/envs/bigthinx/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()
~/opt/anaconda3/envs/bigthinx/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
886 # Lifting succeeded, so variables are initialized and we can run the
887 # stateless function.
--> 888 return self._stateless_fn(*args, **kwds)
889 else:
890 _, _, _, filtered_flat_args = \
~/opt/anaconda3/envs/bigthinx/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
2941 filtered_flat_args) = self._maybe_define_function(args, kwargs)
2942 return graph_function._call_flat(
-> 2943 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access
2944
2945 @property
~/opt/anaconda3/envs/bigthinx/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1917 # No tape is watching; skip to running the function.
1918 return self._build_call_outputs(self._inference_function.call(
-> 1919 ctx, args, cancellation_manager=cancellation_manager))
1920 forward_backward = self._select_forward_and_backward_functions(
1921 args,
~/opt/anaconda3/envs/bigthinx/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
558 inputs=args,
559 attrs=attrs,
--> 560 ctx=ctx)
561 else:
562 outputs = execute.execute_with_cancellation(
~/opt/anaconda3/envs/bigthinx/lib/python3.7/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
InvalidArgumentError: Incompatible shapes: [2,1] vs. [2,31,31]
[[node Equal (defined at <ipython-input-39-739652b805f0>:1) ]] [Op:__inference_train_function_26226]
Function call stack:
train_function
Epoch 1/2
ytrue = [[2]] [1 1] y_pred shape = [1 31 31 3]
one_class_shape = [1 31 31 1]
y_true_shape - [1 1]
y_true_compare_0 = [1 1]
y_true_compare_1 = [1 1]
y_true_compare_0 [[0]]
div_equals_0_check shape = 0
div_equals_1_check shape = 1
after y_true_array = [1 31 31 3] TensorShape([1, 31, 31, 3])
error shape = [1 31 31 3]
loss shape = [] -1222.78308
ytrue = [[2]] [1 1] y_pred shape = [1 31 31 3]
one_class_shape = [1 31 31 1]
y_true_shape - [1 1]
y_true_compare_0 = [1 1]
y_true_compare_1 = [1 1]
y_true_compare_0 [[0]]
div_equals_0_check shape = 0
div_equals_1_check shape = 1
after y_true_array = [1 31 31 3] TensorShape([1, 31, 31, 3])
error shape = [1 31 31 3]
loss shape = [] -8.76188278e-06
1/1 [==============================] - 15s 15s/step - loss: -1222.7831 - mobnet_features_loss: -1222.7831 - Predictions_loss: -8.7619e-06 - mobnet_features_accuracy: 0.3777 - Predictions_accuracy: 0.3777
Epoch 2/2
ytrue = [[2]] [1 1] y_pred shape = [1 31 31 3]
one_class_shape = [1 31 31 1]
y_true_shape - [1 1]
y_true_compare_0 = [1 1]
y_true_compare_1 = [1 1]
y_true_compare_0 [[0]]
div_equals_0_check shape = 0
div_equals_1_check shape = 1
after y_true_array = [1 31 31 3] TensorShape([1, 31, 31, 3])
error shape = [1 31 31 3]
loss shape = [] -4929.66
ytrue = [[2]] [1 1] y_pred shape = [1 31 31 3]
one_class_shape = [1 31 31 1]
y_true_shape - [1 1]
y_true_compare_0 = [1 1]
y_true_compare_1 = [1 1]
y_true_compare_0 [[0]]
div_equals_0_check shape = 0
div_equals_1_check shape = 1
after y_true_array = [1 31 31 3] TensorShape([1, 31, 31, 3])
error shape = [1 31 31 3]
loss shape = [] 4.91845421e-05
1/1 [==============================] - 12s 12s/step - loss: -4929.6602 - mobnet_features_loss: -4929.6602 - Predictions_loss: 4.9185e-05 - mobnet_features_accuracy: 0.3049 - Predictions_accuracy: 0.3049
@tf.function
def grad(model, inputs, targets):
with tf.GradientTape() as tape:
y_pred = model(x)
tf.print("in grad function y_pred shape = ", tf.type_spec_from_value(y_pred))
loss_value = mobilenet_loss(targets, y_pred)
return loss_value, tape.gradient(loss_value, model.trainable_variables)
num_epochs = 2
optimizer = tf.keras.optimizers.Adam()
avg_loss = tf.keras.metrics.Mean()
for epoch in range(num_epochs):
x, y = next(train_generator)
# Optimize the model
loss_value, gradients = grad(model, x, y)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
avg_loss(loss_value)
print(" epoch %d/%d, loss=%.4f "%(epoch +1, num_epochs, avg_loss.result()))