在 Tensorflow 中使用镜像策略时出错

Question

我读取数据并使用以下代码处理它：

data = pd.read_csv('Step1_output.csv')
data = data.sample(frac=1).reset_index(drop=True)
data1 = pd.DataFrame(data, columns=['Res_pair'])

# creating instance of labelencoder
labelencoder = LabelEncoder()
# Assigning numerical values and storing in another column
data1['Res_pair_ID'] = labelencoder.fit_transform(data1['Res_pair'])
data['Res_pair'] = data1['Res_pair_ID']
data = data.to_numpy()
train_X = data[0:data.shape[0],0:566]
train_y = data[0:data.shape[0],566:data.shape[1]]
train_X = train_X.reshape((train_X.shape[0], train_X.shape[1], 1))

我使用以下代码构建模型，其中我尝试使用 Tensorflow 的镜像策略分发数据集：

print("Hyper-parameter values:\n")
print('Momentum Rate =',momentum_rate,'\n')
print('learning rate =',learning_rate,'\n')
print('Number of neurons =',neurons,'\n')

  

strategy = tensorflow.distribute.MirroredStrategy()
with strategy.scope():
        model = tf.keras.Sequential([ 
          tf.keras.layers.Conv1D(64,kernel_size = 3,activation='relu',input_shape=train_X.shape[1:]),
          tf.keras.layers.Flatten(),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(10, activation='softmax'),])
        sgd = optimizers.SGD(lr=learning_rate, decay=1e-6, momentum=momentum_rate, nesterov=True)
        model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy',tensorflow.keras.metrics.Precision()])
        results = model.fit(train_X,train_y,validation_split = 0.2,epochs=10,batch_size = 100)
        print(results)
       
    path = 'saved_model/'
    
    model.save(path, save_format='tf')

    for k in range(100):
        momentum_rate = random.random()
        learning_rate = random.uniform(0,0.2)
        neurons = random.randint(10,50)

我试图在 GPU 上运行代码，但它运行了一段时间，然后抛出此错误：

Hyper-parameter values:

Momentum Rate = 0.6477407029392913

learning rate = 0.03988890117492503

Number of neurons = 35

Epoch 1/10
     1/270110 [..............................] - ETA: 28s - loss: nan - accuracy: 0.0100 - precision: 0.0100Traceback (most recent call last):
  File "parallelised_script_realdata2.py", line 56, in <module>
    results = model.fit(train_X,train_y,validation_split = 0.2,epochs=10,batch_size = 100)
  File "/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 108, in _method_wrapper
    return method(self, *args, **kwargs)
  File "/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 1098, in fit
    tmp_logs = train_function(iterator) 
  File "/usr/local/lib64/python3.6/site-packages/tensorflow/python/eager/def_function.py", line 780, in __call__
    result = self._call(*args, **kwds)
  File "/usr/local/lib64/python3.6/site-packages/tensorflow/python/eager/def_function.py", line 807, in _call
    return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
  File "/usr/local/lib64/python3.6/site-packages/tensorflow/python/eager/function.py", line 2829, in __call__
    return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
  File "/usr/local/lib64/python3.6/site-packages/tensorflow/python/eager/function.py", line 1848, in _filtered_call
    cancellation_manager=cancellation_manager)
  File "/usr/local/lib64/python3.6/site-packages/tensorflow/python/eager/function.py", line 1924, in _call_flat
    ctx, args, cancellation_manager=cancellation_manager))
  File "/usr/local/lib64/python3.6/site-packages/tensorflow/python/eager/function.py", line 550, in call
    ctx=ctx) 
  File "/usr/local/lib64/python3.6/site-packages/tensorflow/python/eager/execute.py", line 60, in quick_execute
    inputs, attrs, num_outputs)
tensorflow.python.framework.errors_impl.InvalidArgumentError:  assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (sequential/dense_4/Softmax:0) = ] [[nan nan nan...]...] [y (Cast_6/x:0) = ] [0]
         [[{{node assert_greater_equal/Assert/AssertGuard/else/_21/assert_greater_equal/Assert/AssertGuard/Assert}}]] [Op:__inference_train_function_1270]

Function call stack:
train_function

更新：如果我不使用strategy = tensorflow.distribute.MirroredStrategy(). 像下面的代码（但对于较大的数据集会因为内存不足而失败）：

def convolutional_neural_network(x, y):
    print("Hyper-parameter values:\n")
    print('Momentum Rate =',momentum_rate,'\n')
    print('learning rate =',learning_rate,'\n')
    print('Number of neurons =',neurons,'\n')

    model = Sequential()
    model.add(Conv1D(filters=64,input_shape=train_X.shape[1:],activation='relu',kernel_size = 3))
    model.add(Flatten())
    model.add(Dense(neurons,activation='relu')) # first hidden layer
    model.add(Dense(neurons, activation='relu')) # second hidden layer
    model.add(Dense(neurons, activation='relu'))
    model.add(Dense(neurons, activation='relu'))
    model.add(Dense(10, activation='softmax'))
    sgd = optimizers.SGD(lr=learning_rate, decay=1e-6, momentum=momentum_rate, nesterov=True)
    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy',tensorflow.keras.metrics.Precision()])

    history = model.fit(train_X, train_y, validation_split=0.2, epochs=10, batch_size=100)



momentum_rate = 0.09
learning_rate = 0.01
neurons = 40
print(convolutional_neural_network(train_X, train_y))

更新 2：仍然面临与较小数据集类似的问题

_________________________________________________________________
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv1d (Conv1D)              (None, 564, 64)           256
_________________________________________________________________
flatten (Flatten)            (None, 36096)             0
_________________________________________________________________
dense (Dense)                (None, 50)                1804850
_________________________________________________________________
dense_1 (Dense)              (None, 50)                2550
_________________________________________________________________
dense_2 (Dense)              (None, 50)                2550
_________________________________________________________________
dense_3 (Dense)              (None, 50)                2550
_________________________________________________________________
dense_4 (Dense)              (None, 10)                510
=================================================================
Total params: 1,813,266
Trainable params: 1,813,266
Non-trainable params: 0

Answer 1

模型定义看起来不错，策略也不错。
您可以验证 train_Y 进行健全性检查吗？大多数情况下我确信错误就在那里。

如果不是这种情况，请尝试model.fit在范围之外运行后者。