我想使用multiclass_roc_auc_score函数评估我的多类序列Keras模型的ROC AUC。我的代码引发了ValueError: Shapes (None, 1) and (None, 4) are incompatible。
我想执行多类分类：
class MulticlassTruePositives(tf.keras.metrics.Metric):
def __init__(self, name='multiclass_true_positives', **kwargs):
super(MulticlassTruePositives, self).__init__(name=name, **kwargs)
self.true_positives = self.add_weight(name='tp', initializer='zeros')
def update_state(self, y_true, y_pred, sample_weight=None):
y_pred = tf.reshape(tf.argmax(y_pred, axis=1), shape=(-1, 1))
values = tf.cast(y_true, 'int32') == tf.cast(y_pred, 'int32')
values = tf.cast(values, 'float32')
if sample_weight is not None:
sample_weight = tf.cast(sample_weight, 'float32')
values = tf.multiply(values, sample_weight)
self.true_positives.assign_add(tf.reduce_sum(values))
def result(self):
return self.true_positives
def reset_states(self):
# The state of the metric will be reset at the start of each epoch.
self.true_positives.assign(0.)
我用以下指标编译了Keras模型：
# Report the AUC of a model outputting a probability.
hypermodel.compile(optimizer='sgd',
loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=[tf.keras.metrics.AUC(), MulticlassTruePositives()])
我实现了Keras回调，它将ROC曲线和Confusion矩阵绘制到一个文件夹中：
class PerformanceVisualizationCallback(Callback):
def __init__(self, model, test_data, image_dir):
super().__init__()
self.model = model
self.test_data = test_data
os.makedirs(image_dir, exist_ok=True)
self.image_dir = image_dir
def on_epoch_end(self, epoch, logs={}):
y_pred = np.asarray(self.model.predict(self.test_data[0]))
y_true = self.test_data[1]
y_pred_class = np.argmax(y_pred, axis=1)
# plot and save confusion matrix
fig, ax = plt.subplots(figsize=(16,12))
plot_confusion_matrix(y_true, y_pred_class, ax=ax)
fig.savefig(os.path.join(self.image_dir, f'confusion_matrix_epoch_{epoch}'))
# plot and save roc curve
fig, ax = plt.subplots(figsize=(16,12))
plot_roc(y_true, y_pred, ax=ax)
fig.savefig(os.path.join(self.image_dir, f'roc_curve_epoch_{epoch}'))
performance_viz_cbk = PerformanceVisualizationCallback(
model=model,
test_data=X_test,
image_dir='perorfmance_charts')
history = hypermodel.fit(x=X_train,
y=y_train,
epochs=5,
callbacks=[performance_viz_cbk])
追溯：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_17/963709483.py in <module>
2 y=y_train,
3 epochs=5,
----> 4 callbacks=[performance_viz_cbk])
/opt/conda/lib/python3.7/site-packages/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)
1182 _r=1):
1183 callbacks.on_train_batch_begin(step)
-> 1184 tmp_logs = self.train_function(iterator)
1185 if data_handler.should_sync:
1186 context.async_wait()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
883
884 with OptionalXlaContext(self._jit_compile):
--> 885 result = self._call(*args, **kwds)
886
887 new_tracing_count = self.experimental_get_tracing_count()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
922 # In this case we have not created variables on the first call. So we can
923 # run the first trace but we should fail if variables are created.
--> 924 results = self._stateful_fn(*args, **kwds)
925 if self._created_variables and not ALLOW_DYNAMIC_VARIABLE_CREATION:
926 raise ValueError("Creating variables on a non-first call to a function"
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
3036 with self._lock:
3037 (graph_function,
-> 3038 filtered_flat_args) = self._maybe_define_function(args, kwargs)
3039 return graph_function._call_flat(
3040 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3458 call_context_key in self._function_cache.missed):
3459 return self._define_function_with_shape_relaxation(
-> 3460 args, kwargs, flat_args, filtered_flat_args, cache_key_context)
3461
3462 self._function_cache.missed.add(call_context_key)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _define_function_with_shape_relaxation(self, args, kwargs, flat_args, filtered_flat_args, cache_key_context)
3380
3381 graph_function = self._create_graph_function(
-> 3382 args, kwargs, override_flat_arg_shapes=relaxed_arg_shapes)
3383 self._function_cache.arg_relaxed[rank_only_cache_key] = graph_function
3384
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3306 arg_names=arg_names,
3307 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3308 capture_by_value=self._capture_by_value),
3309 self._function_attributes,
3310 function_spec=self.function_spec,
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes, acd_record_initial_resource_uses)
1005 _, original_func = tf_decorator.unwrap(python_func)
1006
-> 1007 func_outputs = python_func(*func_args, **func_kwargs)
1008
1009 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
666 # the function a weak reference to itself to avoid a reference cycle.
667 with OptionalXlaContext(compile_with_xla):
--> 668 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
669 return out
670
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
992 except Exception as e: # pylint:disable=broad-except
993 if hasattr(e, "ag_error_metadata"):
--> 994 raise e.ag_error_metadata.to_exception(e)
995 else:
996 raise
ValueError: in user code:
/opt/conda/lib/python3.7/site-packages/keras/engine/training.py:853 train_function *
return step_function(self, iterator)
/opt/conda/lib/python3.7/site-packages/keras/engine/training.py:842 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1286 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2849 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:3632 _call_for_each_replica
return fn(*args, **kwargs)
/opt/conda/lib/python3.7/site-packages/keras/engine/training.py:835 run_step **
outputs = model.train_step(data)
/opt/conda/lib/python3.7/site-packages/keras/engine/training.py:789 train_step
y, y_pred, sample_weight, regularization_losses=self.losses)
/opt/conda/lib/python3.7/site-packages/keras/engine/compile_utils.py:201 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/opt/conda/lib/python3.7/site-packages/keras/losses.py:141 __call__
losses = call_fn(y_true, y_pred)
/opt/conda/lib/python3.7/site-packages/keras/losses.py:245 call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/opt/conda/lib/python3.7/site-packages/keras/losses.py:1666 categorical_crossentropy
y_true, y_pred, from_logits=from_logits, axis=axis)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:206 wrapper
return target(*args, **kwargs)
/opt/conda/lib/python3.7/site-packages/keras/backend.py:4839 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/opt/conda/lib/python3.7/site-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, 1) and (None, 4) are incompatible
我花了一些时间让您的显示图与我的示例数据有意义。
示例：首先，您需要创建与显示计算矩阵匹配的维度输出，请参见损失Fn、优化器、矩阵和网络输出。然后你需要创建预测数据的感觉，它们不允许有多个输入值，然后我花时间让它成为一个重要的样本。最大值或总和是显著的
所有Max的总和并不重要，因为我的队列中的所有优先级都是。
import os
from os.path import exists
import tensorflow as tf
import tensorflow_text as tft
import matplotlib.pyplot as plt
import sklearn.metrics
from sklearn.svm import SVC
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
None
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
config = tf.config.experimental.set_memory_growth(physical_devices[0], True)
print(physical_devices)
print(config)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
list_accuracy = []
checkpoint_path = "F:\models\checkpoint\" + os.path.basename(__file__).split('.')[0] + "\TF_DataSets_01.h5"
checkpoint_dir = os.path.dirname(checkpoint_path)
if not exists(checkpoint_dir) :
os.mkdir(checkpoint_dir)
print("Create directory: " + checkpoint_dir)
input_word = tf.constant(' 'Cause it's easy as an ice cream sundae Slipping outta your hand into the dirt Easy as an ice cream sundae Every dancer gets a little hurt Easy as an ice cream sundae Slipping outta your hand into the dirt Easy as an ice cream sundae Every dancer gets a little hurt Easy as an ice cream sundae Oh, easy as an ice cream sundae ')
dataset = tf.data.Dataset.from_tensors( tf.strings.bytes_split(input_word) )
window_size = 6
dataset = dataset.map( lambda x: tft.sliding_window(x, width=window_size, axis=0) ).flat_map(tf.data.Dataset.from_tensor_slices)
dataset = dataset.batch(1)
list_word = []
label = []
vocab = [ "a", "b", "c", "d", "e", "f", "g", "h", "I", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "_" ]
vocab_hot = [ "ice" ]
layer = tf.keras.layers.StringLookup(vocabulary=vocab)
layer_hot = tf.keras.layers.StringLookup(vocabulary=vocab_hot)
for example in dataset.take(200):
sequences_mapping_string = layer(example[0])
sequences_mapping_string = tf.constant( sequences_mapping_string, shape=(1, 6) )
list_word.append(sequences_mapping_string.numpy())
sequences_mapping_string = tf.reduce_sum(layer_hot( example[0][0] + example[0][1] + example[0][2] ))
sequences_mapping_string = tf.constant( sequences_mapping_string, shape=(1, 1) )
label.append(sequences_mapping_string.numpy())
list_word = tf.constant(list_word, shape=(200, 1, 6, 1), dtype=tf.int64)
label = tf.constant(label, shape=(200, 1, 1, 1), dtype=tf.int64)
dataset = tf.data.Dataset.from_tensor_slices((list_word, label))
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Class / Definition
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
class MulticlassTruePositives(tf.keras.metrics.Metric):
def __init__(self, name='multiclass_true_positives', **kwargs):
super(MulticlassTruePositives, self).__init__(name=name, **kwargs)
self.true_positives = self.add_weight(name='tp', initializer='zeros')
def update_state(self, y_true, y_pred, sample_weight=None):
y_pred = tf.reshape(tf.argmax(y_pred, axis=1), shape=(-1, 1))
values = tf.cast(y_true, 'int32') == tf.cast(y_pred, 'int32')
values = tf.cast(values, 'float32')
if sample_weight is not None:
sample_weight = tf.cast(sample_weight, 'float32')
values = tf.multiply(values, sample_weight)
self.true_positives.assign_add(tf.reduce_sum(values))
def result(self):
return self.true_positives
def reset_state(self):
# The state of the metric will be reset at the start of each epoch.
self.true_positives.assign(0.)
class MyLSTMLayer( tf.keras.layers.LSTM ):
def __init__(self, units, return_sequences, return_state):
super(MyLSTMLayer, self).__init__( units, return_sequences=True, return_state=False )
self.num_units = units
def build(self, input_shape):
self.kernel = self.add_weight("kernel",
shape=[int(input_shape[-1]),
self.num_units])
def call(self, inputs):
return tf.matmul(inputs, self.kernel)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
mycustomlayer = MyLSTMLayer( 64, True, False )
mycustomlayer_2 = MyLSTMLayer( 16, True, False )
model = tf.keras.models.Sequential([
tf.keras.layers.InputLayer(input_shape=(6, 1)),
tf.keras.layers.Embedding(1000, 128, input_length=1),
tf.keras.layers.Reshape(( 6, 128 )),
tf.keras.layers.SpatialDropout1D( rate = 0.4 ),
tf.keras.layers.Conv1D(32, 6, activation="relu"),
tf.keras.layers.MaxPooling1D(strides=1, pool_size=1),
### LSTM
mycustomlayer,
tf.keras.layers.Reshape(( 1, 1, 64 )),
tf.keras.layers.UpSampling2D( size=(4, 4), data_format=None, interpolation='nearest' ),
tf.keras.layers.Conv1D(16, 3, activation="relu"),
tf.keras.layers.Reshape(( 8, 16 )),
tf.keras.layers.MaxPooling1D(),
tf.keras.layers.GlobalMaxPooling1D(),
### LSTM
tf.keras.layers.Reshape(( 1, 16 )),
mycustomlayer_2,
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(128, activation="relu"),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(1),
], name="MyModelClassification")
model.build()
model.summary()
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Callback
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
class PerformanceVisualizationCallback(tf.keras.callbacks.Callback):
def __init__(self, model, test_data, image_dir):
super().__init__()
self.model = model
self.test_data = test_data
self.test_data = tf.constant( self.test_data, shape=(20, 1, 6, 1) )
os.makedirs(image_dir, exist_ok=True)
self.image_dir = image_dir
def on_epoch_end(self, epoch, logs={}):
y_pred = tf.constant(self.model.predict(self.test_data[0])).numpy()
y_true = self.test_data[1]
y_pred_class = tf.math.argmax(y_pred, axis=1).numpy()
clf = SVC(random_state=0)
clf.fit(tf.constant( self.test_data, shape=(20, 6) ).numpy(), tf.cast( tf.linspace( 0, 19, 20, name='linspace', axis=0 ), dtype=tf.int64 ).numpy())
predictions = clf.predict(tf.constant( self.test_data, shape=(20, 6) ).numpy())
cm = sklearn.metrics.confusion_matrix(
[tf.math.argmax(self.test_data[1], axis=1).numpy()[0], tf.math.argmax(self.test_data[2], axis=1).numpy()[0],
tf.math.argmax(self.test_data[3], axis=1).numpy()[0], tf.math.argmax(self.test_data[4], axis=1).numpy()[0], tf.math.argmax(self.test_data[5], axis=1).numpy()[0]],
[1, 2, 3, 4, 5], labels=clf.classes_)
disp = sklearn.metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)
disp.plot()
plt.show()
# fig.savefig(os.path.join(self.image_dir, f'confusion_matrix_epoch_{epoch}'))
clf = sklearn.svm.SVC(random_state=0)
clf.fit(tf.constant( self.test_data, shape=(20, 6) ).numpy(), tf.linspace( 0, 19, 20, name='linspace', axis=0 ).numpy())
if (epoch <= 2):
list_accuracy.append( logs['accuracy'] )
if (epoch == 2):
fpr, tpr, thresholds = sklearn.metrics.roc_curve([0, 0, 1, 1], [0, list_accuracy[0], list_accuracy[1], list_accuracy[2]])
roc_auc = sklearn.metrics.auc(fpr, tpr)
display = sklearn.metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='example estimator')
display.plot()
plt.show()
# fig.savefig(os.path.join(self.image_dir, f'roc_curve_epoch_{epoch}'))
list_word = []
label = []
test_dataset = tf.data.Dataset.from_tensors( tf.strings.bytes_split(input_word) )
test_dataset = test_dataset.map( lambda x: tft.sliding_window(x, width=window_size, axis=0) ).flat_map(tf.data.Dataset.from_tensor_slices)
test_dataset = test_dataset.batch(1)
for example in test_dataset.take(20):
sequences_mapping_string = layer(example[0])
sequences_mapping_string = tf.constant( sequences_mapping_string, shape=(1, 6) )
list_word.append(sequences_mapping_string.numpy())
sequences_mapping_string = tf.reduce_sum(layer_hot( example[0][0] + example[0][1] + example[0][2] ))
sequences_mapping_string = tf.constant( sequences_mapping_string, shape=(1, 1) )
label.append(sequences_mapping_string.numpy())
list_word = tf.constant(list_word, shape=(20, 1, 6, 1), dtype=tf.int64)
label = tf.constant(label, shape=(20, 1, 1, 1), dtype=tf.int64)
test_dataset = tf.data.Dataset.from_tensor_slices((list_word, label))
performance_viz_cbk = PerformanceVisualizationCallback(
model=model,
test_data=list_word,
image_dir='c:perorfmance_charts')
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.SGD(
learning_rate=0.000001,
momentum=0.5,
nesterov=True,
name='SGD',
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
lossfn = tf.keras.losses.BinaryCrossentropy(
from_logits=False,
reduction=tf.keras.losses.Reduction.AUTO,
name='sparse_categorical_crossentropy'
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
# Report the AUC of a model outputting a probability.
model.compile(optimizer=optimizer, loss=lossfn,
metrics=['accuracy', tf.keras.metrics.AUC(), MulticlassTruePositives()])
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: FileWriter
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
if exists(checkpoint_path) :
model.load_weights(checkpoint_path)
print("model load: " + checkpoint_path)
input("Press Any Key!")
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit(dataset, batch_size=100, epochs=3, callbacks=[performance_viz_cbk] )