Colab - Keras - TypeError: 'int' object is not iterable
Colab - Keras - TypeError: 'int' object is not iterable
我试图训练和测试网络以使用 keras mnist 数据集识别数字，它在我的计算机上完美运行，但在 Google Colab 中不起作用。
笔记本设置：
- 硬件加速器GPU
笔记本代码：
%tensorflow_version 2.x
import tensorflow as tf
import keras
import numpy as np
tf.test.gpu_device_name()
from tensorflow.python.client import device_lib
device_lib.list_local_devices()
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras import backend as k
from keras.callbacks import TensorBoard
batchsize=100
num_clases=10
epocas=10 #10*100 = 1000
filas,columnas=28,28
#Entrada y salida
(xt,yt),(xtest, ytest) = mnist.load_data()
xt=xt.reshape(xt.shape[0], filas, columnas, 1)
xtest=xtest.reshape(xtest.shape[0], filas, columnas, 1)
xt=xt.astype('float32')
xtest=xtest.astype('float32')
xt=xt/255
xtest=xtest/255
yt=keras.utils.to_categorical(yt,num_clases)
ytest=keras.utils.to_categorical(ytest,num_clases)
modelo=Sequential()
modelo.add(Conv2D(64, kernel_size=(3,3), activation='relu', input_shape=(28,28,1)))
modelo.add(Conv2D(128, kernel_size=(3,3), activation='relu'))
modelo.add(MaxPooling2D(pool_size=(2,3)))
modelo.add(Flatten())
modelo.add(Dense(70,activation='relu'))
modelo.add(Dropout(0,25))
modelo.add(Dense(num_clases, activation='softmax'))
modelo.compile(loss=keras.losses.categorical_crossentropy, metrics=['categorical_accuracy'], optimizer=keras.optimizers.Adam())
modelo.fit(xt, yt, batch_size=batchsize, epochs=epocas, validation_data=[xtest, ytest], verbose=1)
puntuacion=modelo.evaluate(xtest, ytest, batch_size=batchsize)
print(puntuacion)
这是 Google Colab 向我展示的完整错误日志：
TypeError Traceback (most recent call last)
<ipython-input-19-cac37ad8c603> in <module>()
45 modelo.compile(loss=keras.losses.categorical_crossentropy, metrics=['categorical_accuracy'], optimizer=keras.optimizers.Adam())
46
---> 47 modelo.fit(xt, yt, batch_size=batchsize, epochs=epocas, validation_data=[xtest, ytest], verbose=1)
48
49 puntuacion=modelo.evaluate(xtest, ytest, batch_size=batchsize)
/usr/local/lib/python3.7/dist-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()
/usr/local/lib/python3.7/dist-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()
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
869 # This is the first call of __call__, so we have to initialize.
870 initializers = []
--> 871 self._initialize(args, kwds, add_initializers_to=initializers)
872 finally:
873 # At this point we know that the initialization is complete (or less
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
724 self._concrete_stateful_fn = (
725 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 726 *args, **kwds))
727
728 def invalid_creator_scope(*unused_args, **unused_kwds):
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2967 args, kwargs = None, None
2968 with self._lock:
-> 2969 graph_function, _ = self._maybe_define_function(args, kwargs)
2970 return graph_function
2971
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3359
3360 self._function_cache.missed.add(call_context_key)
-> 3361 graph_function = self._create_graph_function(args, kwargs)
3362 self._function_cache.primary[cache_key] = graph_function
3363
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3204 arg_names=arg_names,
3205 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3206 capture_by_value=self._capture_by_value),
3207 self._function_attributes,
3208 function_spec=self.function_spec,
/usr/local/lib/python3.7/dist-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)
988 _, original_func = tf_decorator.unwrap(python_func)
989
--> 990 func_outputs = python_func(*func_args, **func_kwargs)
991
992 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
632 xla_context.Exit()
633 else:
--> 634 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
635 return out
636
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise
TypeError: in user code:
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 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:2730 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:3417 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:754 train_step
y_pred = self(x, training=True)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1012 __call__
outputs = call_fn(inputs, *args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:375 call
return super(Sequential, self).call(inputs, training=training, mask=mask)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py:425 call
inputs, training=training, mask=mask)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py:560 _run_internal_graph
outputs = node.layer(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1012 __call__
outputs = call_fn(inputs, *args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/core.py:231 call
lambda: array_ops.identity(inputs))
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/control_flow_util.py:115 smart_cond
pred, true_fn=true_fn, false_fn=false_fn, name=name)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/smart_cond.py:54 smart_cond
return true_fn()
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/core.py:226 dropped_inputs
noise_shape=self._get_noise_shape(inputs),
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/core.py:215 _get_noise_shape
for i, value in enumerate(self.noise_shape):
TypeError: 'int' object is not iterable
为什么我会收到这个错误？
问题出在 Dropout 层的输入上
modelo.add(Dropout(0,25))
第一个参数接受要丢弃的输入单位的分数。
第二个是一维整数张量，表示将与输入相乘的二元 dropout mask 的形状。
我想你的意思是只设置第一个参数。如果将其更改为下面的行，它将 运行.
modelo.add(Dropout(0.4))
另一个问题是验证数据的输入必须是元组而不是数组，因此也更改此行：
modelo.fit(xt, y=yt, batch_size=batchsize, epochs=epocas, validation_data=(xtest, ytest), verbose=1)
训练和评估的输出：
Epoch 10/10
600/600 [==============================] - 4s 7ms/step - loss: 0.0225 - categorical_accuracy: 0.9924 - val_loss: 0.0280 - val_categorical_accuracy: 0.9933
100/100 [==============================] - 0s 3ms/step - loss: 0.0280 - categorical_accuracy: 0.9933
[0.0280386321246624, 0.9933000206947327]
我试图训练和测试网络以使用 keras mnist 数据集识别数字，它在我的计算机上完美运行，但在 Google Colab 中不起作用。
笔记本设置：
- 硬件加速器GPU
笔记本代码：
%tensorflow_version 2.x
import tensorflow as tf
import keras
import numpy as np
tf.test.gpu_device_name()
from tensorflow.python.client import device_lib
device_lib.list_local_devices()
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras import backend as k
from keras.callbacks import TensorBoard
batchsize=100
num_clases=10
epocas=10 #10*100 = 1000
filas,columnas=28,28
#Entrada y salida
(xt,yt),(xtest, ytest) = mnist.load_data()
xt=xt.reshape(xt.shape[0], filas, columnas, 1)
xtest=xtest.reshape(xtest.shape[0], filas, columnas, 1)
xt=xt.astype('float32')
xtest=xtest.astype('float32')
xt=xt/255
xtest=xtest/255
yt=keras.utils.to_categorical(yt,num_clases)
ytest=keras.utils.to_categorical(ytest,num_clases)
modelo=Sequential()
modelo.add(Conv2D(64, kernel_size=(3,3), activation='relu', input_shape=(28,28,1)))
modelo.add(Conv2D(128, kernel_size=(3,3), activation='relu'))
modelo.add(MaxPooling2D(pool_size=(2,3)))
modelo.add(Flatten())
modelo.add(Dense(70,activation='relu'))
modelo.add(Dropout(0,25))
modelo.add(Dense(num_clases, activation='softmax'))
modelo.compile(loss=keras.losses.categorical_crossentropy, metrics=['categorical_accuracy'], optimizer=keras.optimizers.Adam())
modelo.fit(xt, yt, batch_size=batchsize, epochs=epocas, validation_data=[xtest, ytest], verbose=1)
puntuacion=modelo.evaluate(xtest, ytest, batch_size=batchsize)
print(puntuacion)
这是 Google Colab 向我展示的完整错误日志：
TypeError Traceback (most recent call last)
<ipython-input-19-cac37ad8c603> in <module>()
45 modelo.compile(loss=keras.losses.categorical_crossentropy, metrics=['categorical_accuracy'], optimizer=keras.optimizers.Adam())
46
---> 47 modelo.fit(xt, yt, batch_size=batchsize, epochs=epocas, validation_data=[xtest, ytest], verbose=1)
48
49 puntuacion=modelo.evaluate(xtest, ytest, batch_size=batchsize)
/usr/local/lib/python3.7/dist-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()
/usr/local/lib/python3.7/dist-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()
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
869 # This is the first call of __call__, so we have to initialize.
870 initializers = []
--> 871 self._initialize(args, kwds, add_initializers_to=initializers)
872 finally:
873 # At this point we know that the initialization is complete (or less
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
724 self._concrete_stateful_fn = (
725 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 726 *args, **kwds))
727
728 def invalid_creator_scope(*unused_args, **unused_kwds):
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2967 args, kwargs = None, None
2968 with self._lock:
-> 2969 graph_function, _ = self._maybe_define_function(args, kwargs)
2970 return graph_function
2971
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3359
3360 self._function_cache.missed.add(call_context_key)
-> 3361 graph_function = self._create_graph_function(args, kwargs)
3362 self._function_cache.primary[cache_key] = graph_function
3363
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3204 arg_names=arg_names,
3205 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3206 capture_by_value=self._capture_by_value),
3207 self._function_attributes,
3208 function_spec=self.function_spec,
/usr/local/lib/python3.7/dist-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)
988 _, original_func = tf_decorator.unwrap(python_func)
989
--> 990 func_outputs = python_func(*func_args, **func_kwargs)
991
992 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
632 xla_context.Exit()
633 else:
--> 634 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
635 return out
636
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise
TypeError: in user code:
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 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:2730 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:3417 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:754 train_step
y_pred = self(x, training=True)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1012 __call__
outputs = call_fn(inputs, *args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:375 call
return super(Sequential, self).call(inputs, training=training, mask=mask)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py:425 call
inputs, training=training, mask=mask)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py:560 _run_internal_graph
outputs = node.layer(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1012 __call__
outputs = call_fn(inputs, *args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/core.py:231 call
lambda: array_ops.identity(inputs))
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/control_flow_util.py:115 smart_cond
pred, true_fn=true_fn, false_fn=false_fn, name=name)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/smart_cond.py:54 smart_cond
return true_fn()
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/core.py:226 dropped_inputs
noise_shape=self._get_noise_shape(inputs),
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/core.py:215 _get_noise_shape
for i, value in enumerate(self.noise_shape):
TypeError: 'int' object is not iterable
为什么我会收到这个错误？
问题出在 Dropout 层的输入上
modelo.add(Dropout(0,25))
第一个参数接受要丢弃的输入单位的分数。 第二个是一维整数张量，表示将与输入相乘的二元 dropout mask 的形状。 我想你的意思是只设置第一个参数。如果将其更改为下面的行，它将 运行.
modelo.add(Dropout(0.4))
另一个问题是验证数据的输入必须是元组而不是数组，因此也更改此行：
modelo.fit(xt, y=yt, batch_size=batchsize, epochs=epocas, validation_data=(xtest, ytest), verbose=1)
训练和评估的输出：
Epoch 10/10
600/600 [==============================] - 4s 7ms/step - loss: 0.0225 - categorical_accuracy: 0.9924 - val_loss: 0.0280 - val_categorical_accuracy: 0.9933
100/100 [==============================] - 0s 3ms/step - loss: 0.0280 - categorical_accuracy: 0.9933
[0.0280386321246624, 0.9933000206947327]