238. Product of Array Except Self- 非自身数组的乘积

本文提供了一种解决LeetCode第238题“除自身以外数组的乘积”的方法,该方法使用O(n)的时间复杂度,并考虑了数组中包含0的情况。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

238. Product of Array Except Self

Given an array of n integers where n > 1, nums, return an array output such that output[i] is equal to the product of all the elements of nums except nums[i].

Solve it without division and in O(n).

For example, given [1,2,3,4], return [24,12,8,6].

Follow up:
Could you solve it with constant space complexity? (Note: The output array does not count as extra space for the purpose of space complexity analysis.)

public class Solution {
    public int[] productExceptSelf(int[] nums) {
        int count = 1;
        int count1 = 1;
        HashSet<Integer> set= new HashSet<Integer>();
        int[] result= new int[nums.length];
        for(int i = 0; i< nums.length; i++){
            count*=nums[i];
            if(nums[i]==0&&set.add(nums[i])) {
            	set.add(nums[i]);
            	continue;
            }
            count1*=nums[i];
        }
        for(int i = 0; i< nums.length; i++){
            if(nums[i]!=0) result[i]=count/nums[i];
            if(nums[i]==0) result[i]= count1;
        }
        return result;
    }
}
 感觉想的有点麻烦,应该有简单方法。TO be  continue……

原文链接:https://leetcode.com/problems/product-of-array-except-self/


NotImplementedError: Cannot convert a symbolic Tensor (lstm_1/strided_slice:0) to a numpy array. --------------------------------------------------------------------------- NotImplementedError Traceback (most recent call last) Cell In[20], line 51 48 start_time = time.time() 50 # 选择要训练的模型类型(rnn/lstm/gru) ---> 51 train_model(model_type='lstm') 53 # 计算总耗时 54 total_time = time.time() - start_time Cell In[20], line 13, in train_model(model_type) 11 model = build_gru_model() 12 else: # 默认使用LSTM ---> 13 model = build_lstm_model() 15 # 打印模型结构 16 model.summary() Cell In[16], line 2, in build_lstm_model() 1 def build_lstm_model(): ----> 2 model = keras.Sequential([ 3 layers.Embedding(total_words, embedding_len, input_length=max_review_len), 4 layers.LSTM(64, return_sequences=False), 5 layers.Dense(1, activation='sigmoid') 6 ]) 7 return model File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/training/tracking/base.py:457, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs) 455 self._self_setattr_tracking = False # pylint: disable=protected-access 456 try: --> 457 result = method(self, *args, **kwargs) 458 finally: 459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/engine/sequential.py:113, in Sequential.__init__(self, layers, name) 111 tf_utils.assert_no_legacy_layers(layers) 112 for layer in layers: --> 113 self.add(layer) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/training/tracking/base.py:457, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs) 455 self._self_setattr_tracking = False # pylint: disable=protected-access 456 try: --> 457 result = method(self, *args, **kwargs) 458 finally: 459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/engine/sequential.py:195, in Sequential.add(self, layer) 190 self.inputs = layer_utils.get_source_inputs(self.outputs[0]) 192 elif self.outputs: 193 # If the model is being built continuously on top of an input layer: 194 # refresh its output. --> 195 output_tensor = layer(self.outputs[0]) 196 if len(nest.flatten(output_tensor)) != 1: 197 raise TypeError('All layers in a Sequential model ' 198 'should have a single output tensor. ' 199 'For multi-output layers, ' 200 'use the functional API.') File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:623, in RNN.__call__(self, inputs, initial_state, constants, **kwargs) 617 inputs, initial_state, constants = _standardize_args(inputs, 618 initial_state, 619 constants, 620 self._num_constants) 622 if initial_state is None and constants is None: --> 623 return super(RNN, self).__call__(inputs, **kwargs) 625 # If any of `initial_state` or `constants` are specified and are Keras 626 # tensors, then add them to the inputs and temporarily modify the 627 # input_spec to include them. 629 additional_inputs = [] File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/engine/base_layer.py:854, in Layer.__call__(self, inputs, *args, **kwargs) 852 outputs = base_layer_utils.mark_as_return(outputs, acd) 853 else: --> 854 outputs = call_fn(cast_inputs, *args, **kwargs) 856 except errors.OperatorNotAllowedInGraphError as e: 857 raise TypeError('You are attempting to use Python control ' 858 'flow in a layer that was not declared to be ' 859 'dynamic. Pass `dynamic=True` to the class ' 860 'constructor.\nEncountered error:\n"""\n' + 861 str(e) + '\n"""') File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2548, in LSTM.call(self, inputs, mask, training, initial_state) 2546 self.cell.reset_dropout_mask() 2547 self.cell.reset_recurrent_dropout_mask() -> 2548 return super(LSTM, self).call( 2549 inputs, mask=mask, training=training, initial_state=initial_state) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:681, in RNN.call(self, inputs, mask, training, initial_state, constants) 675 def call(self, 676 inputs, 677 mask=None, 678 training=None, 679 initial_state=None, 680 constants=None): --> 681 inputs, initial_state, constants = self._process_inputs( 682 inputs, initial_state, constants) 684 if mask is not None: 685 # Time step masks must be the same for each input. 686 # TODO(scottzhu): Should we accept multiple different masks? 687 mask = nest.flatten(mask)[0] File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:798, in RNN._process_inputs(self, inputs, initial_state, constants) 796 initial_state = self.states 797 else: --> 798 initial_state = self.get_initial_state(inputs) 800 if len(initial_state) != len(self.states): 801 raise ValueError('Layer has ' + str(len(self.states)) + 802 ' states but was passed ' + str(len(initial_state)) + 803 ' initial states.') File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:605, in RNN.get_initial_state(self, inputs) 603 dtype = inputs.dtype 604 if get_initial_state_fn: --> 605 init_state = get_initial_state_fn( 606 inputs=None, batch_size=batch_size, dtype=dtype) 607 else: 608 init_state = _generate_zero_filled_state(batch_size, self.cell.state_size, 609 dtype) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2313, in LSTMCell.get_initial_state(self, inputs, batch_size, dtype) 2312 def get_initial_state(self, inputs=None, batch_size=None, dtype=None): -> 2313 return list(_generate_zero_filled_state_for_cell( 2314 self, inputs, batch_size, dtype)) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2752, in _generate_zero_filled_state_for_cell(cell, inputs, batch_size, dtype) 2750 batch_size = array_ops.shape(inputs)[0] 2751 dtype = inputs.dtype -> 2752 return _generate_zero_filled_state(batch_size, cell.state_size, dtype) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2768, in _generate_zero_filled_state(batch_size_tensor, state_size, dtype) 2765 return array_ops.zeros(init_state_size, dtype=dtype) 2767 if nest.is_sequence(state_size): -> 2768 return nest.map_structure(create_zeros, state_size) 2769 else: 2770 return create_zeros(state_size) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/util/nest.py:536, in map_structure(func, *structure, **kwargs) 532 flat_structure = [flatten(s, expand_composites) for s in structure] 533 entries = zip(*flat_structure) 535 return pack_sequence_as( --> 536 structure[0], [func(*x) for x in entries], 537 expand_composites=expand_composites) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/util/nest.py:536, in <listcomp>(.0) 532 flat_structure = [flatten(s, expand_composites) for s in structure] 533 entries = zip(*flat_structure) 535 return pack_sequence_as( --> 536 structure[0], [func(*x) for x in entries], 537 expand_composites=expand_composites) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2765, in _generate_zero_filled_state.<locals>.create_zeros(unnested_state_size) 2763 flat_dims = tensor_shape.as_shape(unnested_state_size).as_list() 2764 init_state_size = [batch_size_tensor] + flat_dims -> 2765 return array_ops.zeros(init_state_size, dtype=dtype) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/ops/array_ops.py:2338, in zeros(shape, dtype, name) 2334 if not isinstance(shape, ops.Tensor): 2335 try: 2336 # Create a constant if it won't be very big. Otherwise create a fill op 2337 # to prevent serialized GraphDefs from becoming too large. -> 2338 output = _constant_if_small(zero, shape, dtype, name) 2339 if output is not None: 2340 return output File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/ops/array_ops.py:2295, in _constant_if_small(value, shape, dtype, name) 2293 def _constant_if_small(value, shape, dtype, name): 2294 try: -> 2295 if np.prod(shape) < 1000: 2296 return constant(value, shape=shape, dtype=dtype, name=name) 2297 except TypeError: 2298 # Happens when shape is a Tensor, list with Tensor elements, etc. File <__array_function__ internals>:180, in prod(*args, **kwargs) File /opt/conda/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3088, in prod(a, axis, dtype, out, keepdims, initial, where) 2970 @array_function_dispatch(_prod_dispatcher) 2971 def prod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue, 2972 initial=np._NoValue, where=np._NoValue): 2973 """ 2974 Return the product of array elements over a given axis. 2975 (...) 3086 10 3087 """ -> 3088 return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out, 3089 keepdims=keepdims, initial=initial, where=where) File /opt/conda/lib/python3.8/site-packages/numpy/core/fromnumeric.py:86, in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs) 83 else: 84 return reduction(axis=axis, out=out, **passkwargs) ---> 86 return ufunc.reduce(obj, axis, dtype, out, **passkwargs) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/framework/ops.py:735, in Tensor.__array__(self) 734 def __array__(self): --> 735 raise NotImplementedError("Cannot convert a symbolic Tensor ({}) to a numpy" 736 " array.".format(self.name)) NotImplementedError: Cannot convert a symbolic Tensor (lstm_1/strided_slice:0) to a numpy array. + Code + Markdown ​ + Code + Markdown 4.使用训练好的模型预测文本类型 + Code + Markdown #选做 ​ + Code + Markdown keras关于fit方法中的参数定义如下 def fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=‘auto’, callbacks=None, validation_split=0., validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_batch_size=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False): + Code + Markdown
06-22
<think>我们遇到的问题是在构建LSTM模型时出现的错误,错误信息是:`NotImplementedError:CannotconvertasymbolicTensor(lstm_1/strided_slice:0)toanumpyarray.`。这个错误通常发生在试图将TensorFlow的符号张量(symbolictensor)转换为NumPy数组时,因为NumPy操作无法在符号张量上执行。错误堆栈显示,在创建LSTM的初始状态时,调用了`array_ops.zeros`,而其中尝试计算了形状的乘积(`np.prod(shape)`)。由于`shape`是一个符号张量(而不是具体的数值),NumPy的`prod`函数无法处理,因此抛出异常。这个问题通常是由于TensorFlow和NumPy版本不兼容或代码中的某些操作不支持符号张量导致的。在TensorFlow2.x中,我们应避免在模型构建过程中使用NumPy操作,因为模型构建阶段使用的是符号张量(图形模式)。具体到我们的代码,错误发生在构建LSTM模型时,具体是在创建初始状态的时候。我们使用的是Keras的`Sequential`模型和`LSTM`层。在Keras中,我们通常不需要显式处理初始状态,除有特殊需求。因此,这个问题可能是由于Keras内部实现或环境问题引起的。###解决方案1.**检查TensorFlow和NumPy版本**:确保使用兼容的版本。建议使用TensorFlow2.2及以上版本,并确保NumPy是最新版本(但不要超过TensorFlow支持的范围)。可以通过以下命令升级:```bashpipinstall--upgradetensorflownumpy```2.**避免在模型构建中使用NumPy操作**:我们的代码中并没有直接使用NumPy,但错误发生在TensorFlow内部。因此,我们需要检查环境。3.**使用TensorFlow操作替代NumPy操作**:由于错误发生在TensorFlow内部,我们无法直接修改。但我们可以尝试改变模型构建方式。4.**使用`tf.function`或改变模型构建方式**:有时,将模型构建代码包装在`tf.function`中可能会解决,但这里不适用,因为我们在构建模型,而不是在训练步骤。5.**使用`tf.config.run_functions_eagerly(True)`**:将TensorFlow设置为立即执行模式(eagerexecution)可能会避免这个问题,但这会影响性能,不推荐长期使用。然而,从错误堆栈来看,问题出在LSTM层内部。因此,更可能的是版本问题。但如果我们无法升级,我们可以尝试以下变通方法:###变通方法我们可以尝试使用函数式API构建模型,而不是`Sequential`模型。有时,函数式API可以避免一些内部错误。将构建LSTM模型的代码从`Sequential`改为函数式API:原始代码(Sequential):```pythondefbuild_lstm_model():model=keras.Sequential([layers.Embedding(total_words,embedding_len,input_length=max_review_len),layers.LSTM(64,return_sequences=False),layers.Dense(1,activation='sigmoid')])returnmodel```改为函数式API:```pythondefbuild_lstm_model():inputs=keras.Input(shape=(max_review_len,))x=layers.Embedding(total_words,embedding_len)(inputs)x=layers.LSTM(64,return_sequences=False)(x)outputs=layers.Dense(1,activation='sigmoid')(x)model=keras.Model(inputs=inputs,outputs=outputs)returnmodel```同样,也可以尝试为LSTM层指定一个初始状态,但通常不需要。###其他建议-确保没有在模型构建的任何部分使用NumPy操作(比如在自定义层或损失函数中),但我们的代码中没有自定义层。-如果问题仍然存在,尝试重启内核或重新安装TensorFlow。###示例代码(修改后的LSTM模型构建)```pythonfromtensorflow.kerasimportlayers,Modeldefbuild_lstm_model():inputs=layers.Input(shape=(max_review_len,))embedding=layers.Embedding(total_words,embedding_len)(inputs)lstm=layers.LSTM(64,return_sequences=False)(embedding)outputs=layers.Dense(1,activation='sigmoid')(lstm)model=Model(inputs=inputs,outputs=outputs)returnmodel```###完整训练代码调整将`train_model`函数中构建模型的部分改为上述函数式API。###解释函数式API提供了更灵活的模型构建方式,可能避免了一些`Sequential`模型中的内部问题。同时,我们确保在模型构建过程中不使用任何可能触发符号张量转NumPy数组的操作。###其他相关问题如果上述方法仍不能解决问题,我们可能需要考虑以下方面:-检查输入数据的形状和类型是否正确。-确保`max_review_len`、`total_words`、`embedding_len`等参数是整数,而不是张量或None。-尝试使用更小的模型或数据子集进行测试,以排除内存问题。###相关问题§§相关问题§§:1.在TensorFlow中,符号张量和即时执行模式有什么区别?2.如何检查TensorFlow和NumPy的版本兼容性?3.除了函数式API,还有哪些方法可以构建Keras模型?4.在构建RNN模型时,如何处理变长输入序列?</think>### 问题分析 您遇到的错误 `NotImplementedError: Cannot convert a symbolic Tensor (lstm_1/strided_slice:0) to a numpy array.` 是 TensorFlow/Keras 中常见的问题,主要发生在: 1. **TensorFlow 与 NumPy 版本不兼容**:较新的 TensorFlow 版本(2.4+)与某些 NumPy 版本存在兼容性问题 2. **符号张量操作**:Keras 在构建模型图时使用符号张量,这些张量不能直接转换为 NumPy 数组 3. **模型构建顺序问题**:在模型编译前尝试访问某些属性或执行操作 ### 解决方案 #### 方法 1:升级 TensorFlow 和 NumPy(推荐) ```bash pip install --upgrade tensorflow numpy ``` #### 方法 2:修改模型构建代码(使用函数式 API) ```python from tensorflow.keras import Input, Model def build_lstm_model(): inputs = Input(shape=(max_review_len,)) x = layers.Embedding(total_words, embedding_len)(inputs) x = layers.LSTM(64, return_sequences=False)(x) outputs = layers.Dense(1, activation='sigmoid')(x) model = Model(inputs=inputs, outputs=outputs) return model ``` #### 方法 3:添加延迟初始化(避免符号张量问题) ```python def build_lstm_model(): model = keras.Sequential() model.add(layers.Embedding(total_words, embedding_len, input_length=max_review_len)) model.add(layers.LSTM(64)) model.add(layers.Dense(1, activation='sigmoid')) return model ``` ### 完整修复代码 ```python import tensorflow as tf from tensorflow.keras import layers, models import numpy as np # 确保使用兼容版本 print(f"TensorFlow: {tf.__version__}") print(f"NumPy: {np.__version__}") # 模型构建函数(修复版) def build_lstm_model(): model = models.Sequential([ layers.Embedding(input_dim=total_words, output_dim=embedding_len, input_length=max_review_len), layers.LSTM(64, return_sequences=False), layers.Dense(1, activation='sigmoid') ]) return model # 训练函数 def train_model(model_type='lstm'): if model_type == 'rnn': model = build_rnn_model() elif model_type == 'gru': model = build_gru_model() else: model = build_lstm_model() model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) model.summary() # 训练模型(添加验证集) history = model.fit( x_train, y_train, epochs=10, batch_size=32, validation_split=0.2, # 使用20%数据作为验证集 verbose=1 ) return model, history # 使用示例 total_words = 10000 # 词汇表大小 embedding_len = 128 # 嵌入维度 max_review_len = 200 # 最大评论长度 # 假设已有预处理数据 # x_train, y_train = ... model, history = train_model(model_type='lstm') ``` ### 关键修复点解释 1. **版本兼容性检查**: ```python print(f"TensorFlow: {tf.__version__}") print(f"NumPy: {np.__version__}") ``` 确保使用兼容版本(推荐 TF ≥ 2.4, NumPy ≥ 1.19) 2. **函数式 API 构建**: 使用 `Input` 层明确指定输入形状,避免符号张量问题: ```python inputs = Input(shape=(max_review_len,)) ``` 3. **延迟添加层**: ```python model = models.Sequential() model.add(...) ``` 避免在初始化时传递层列表可能引发的问题 4. **添加验证集**: ```python validation_split=0.2 ``` 监控模型在训练过程中的泛化能力 ### 预测文本类型示例代码 ```python def predict_text(model, text_sequence, max_len=200): # 预处理文本(需要与训练相同的预处理) sequence = pad_sequences([text_sequence], maxlen=max_len) # 预测 prediction = model.predict(sequence) # 输出结果 sentiment = "Positive" if prediction[0] > 0.5 else "Negative" confidence = prediction[0][0] if prediction[0] > 0.5 else 1 - prediction[0][0] print(f"Sentiment: {sentiment} (Confidence: {confidence:.2%})") return sentiment, confidence # 使用示例 sample_text = "This movie was absolutely fantastic! The acting was superb." predicted_sentiment = predict_text(model, sample_text) ``` ### Keras fit 参数使用建议 ```python history = model.fit( x=x_train, # 训练数据 y=y_train, # 标签 batch_size=64, # 批次大小(根据显存调整) epochs=20, # 训练轮次 verbose=1, # 显示进度条 callbacks=[ # 回调函数 tf.keras.callbacks.EarlyStopping(patience=3), tf.keras.callbacks.ModelCheckpoint('best_model.h5') ], validation_split=0.2, # 自动分割验证集 # validation_data=(x_val, y_val), # 或手动指定验证集 shuffle=True, # 每个epoch前打乱数据 class_weight={0: 1, 1: 1.5} if class_imbalance else None, # 处理不平衡数据 initial_epoch=0, # 从指定epoch开始 steps_per_epoch=len(x_train)//64, # 每个epoch步数 validation_freq=1 # 每个epoch后验证 ) ```
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值