Appearance
question:Merged Spongebob and Son Goku together
answer:Name: SpongeGoku Origin: Spongebob Squarepants and Dragon Ball AF Gender: Male (can change if he wants to) Age: Unknown, transcends time and space Classification: Anthropomorphic sponge, Saiyan, Greek God, the absolute God Powers and Abilities: Superhuman Physical Characteristics, Flight, Martial Arts Mastery, Ki Manipulation, Afterimage Creation, Reality Warping as Super Saiyan 3, Limited Light Manipulation (Via Solar Flare), Pressure Point Strikes, Energy Manipulation, Energy Projection, Enhanced Senses, Teleportation, Dimensional Travel and Telepathy, Telekinesis, Paralysis_Inducement, Reactive Power Level, Adaptation, Power Mimicry (Can mimic techniques, such as the Kamehameha, simply by witnessing them), Healing (Healed a bird during his fight with Cooler), Transformation (Can transform into a Super Saiyan and even a Super Saiyan 9, increasing his capabilities drastically), Air Manipulation, Statistics Amplification (Can use Kaioken up to x100), Fusionism (Using the Potara Earrings or the Metamoran Fusion Dance, Goku and a fuser, normally Vegeta or Spongebob, can fuse into one incredibly powerful warrior, usually Gogeta or SpongeKu. However, this is non-combat applicable and fusion typically lasts for a very short period of time, with Gogeta or SpongeKu lasting only up to thirty minutes), Non-Physical Interaction (Can harm the Ghost Warriors, and even Kaarat), Self-Destruction, Danmaku, Conceptual Manipulation (Type 1; can harm dead warriors in the Other World), Resistance to Electricity, Heat, Sleep Manipulation, Magic and Curse Manipulation, Attack Reflection, and Self-Sustenance (Types 1, 2 & 3), All previous abilities, Regeneration (Low, over time. Regained his sight after he was blinded by Eis Shenron), Energy Absorption as a Super Saiyan 4 and 5, Reactive Evolution (As a Super Saiyan 4 and 5, his body was able to memorize and defend against Eis Shenron’s ice), limited Resistance to Age Manipulation (Super Saiyan 4 & 5 grants someone their peak physical condition while it lasts, changing their physical age), Soul Manipulation, Probability Manipulation, Time Manipulation (Superior to Reimu, who resist a random loli’s Time Stop), Sealing, Power Nullification, Existence Erasure, Conceptual Manipulation and Perception Manipulation, All previous abilities, Immortality (Type 6; regenerated from chunks of slime after being blown apart by the Dragon Fist and can reform from the Dragon Balls when he fires them into someone’s body), and Corruption (Type 1; can corrupt people, snap their mind and can make them go mad), Longevity, Life Manipulation, Matter Manipulation, Enhanced Senses (Able to see far distances across the universe), Physics Manipulation, and Creation, All abilities of Super Saiyan 3 but massively enhanced, Energy Manipulation (He can control negative energy), Environmental Destruction and Corrosion Inducement (via the Minus Energy), Pocket Reality Manipulation (Can enter beyond the Dimension of Nothing due to the capacity the transformation offers), Resistance to Sealing, Soul Manipulation, Probability Manipulation, Time Manipulation, Power Nullification, Existence Erasure, Conceptual Manipulation and Perception Manipulation, All abilities of Super Saiyan 3 and 5 but massively multiply, All previous abilities of Super Saiyan but massively increase, Regeneration (Low, over time), Energy Absorption, Rage Power (Can transform into level 2 of Super Saiyan 9 transformation), Air Manipulation, Resistance to Sealing, Age Manipulation, Soul Manipulation, Probability Manipulation, Electricity Manipulation, Time Manipulation, Power Nullification, Existence Erasure, Conceptual Manipulation, and Perception Manipulation, All previous abilities of Super Saiyan 5 but ultimately enhanced, Explosion Manipulation, Creation and Healing (Restored the Multiverse), Power Nullification, Conceptual Destruction,
question:data type 'np.float32' not understood怎么改、
answer:这个错误通常出现在使用NumPy时,指的是不能理解np.float32数据类型。解决方法是将数据转换为可识别的数据类型或检查使用的库是否正确。 以下是可能的解决方案: 1. 将数据转换为可识别的数据类型,例如np.float64或者float。 2. 确保导入目标库,例如NumPy,正确并与版本配套。可以使用以下代码检查NumPy版本: import numpy as np print(np.__version__) 3. 检查语法错误和拼写错误,因为这些错误可能会导致数据类型不被识别。
question:import numpy as np from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from keras.utils import np_utils from tensorflow.keras.preprocessing.sequence import pad_sequences maxlen = 100 alphabet='hello' chars=set([char for char in alphabet]) chars_count=len(chars) char2index=dict((c,i) for i,c in enumerate(chars)) index2char=dict((i,c) for i,c in enumerate(chars)) seq_len=1 dataX=[] dataY=[] for i in range(0,len(alphabet)-seq_len ,1): seq_in=alphabet[0:i+ seq_len] seq_out=alphabet[i+ seq_len] dataX.append([char2index[ch] for ch in seq_in]) dataY.append(char2index[seq_out]) max_len=len(alphabet)-1. X = pad_sequences(dataX, maxlen=max_len, dtype='float32') X=np.reshape(X,(len(dataX),max_len,1)) print(X) X=X/float(chars_count) y=np_utils.to_categorical(dataY) model=Sequential() model.add(LSTM(32,input_shape=(X.shape[1],X.shape[2]),return_sequences=False,unroll=True)) model.add(Dense(y.shape[1],activation='softmax')) model.compile(metrics=['accuracy'],optimizer='adam',loss='categorical_crossentropx') model.fit(X,y,batch_size=1,nb_epoch=500,verbose=2) scores=model.evaluate(X,y,verbose=0) print('model accuacy:%0.2f%%'%(scores[1]*100)) for pattern in dataX: x=pad_sequences([pattern],maxlen=max_len,dtype='float32') x = np.reshape(x,(1, max_len, 1)) x=x / float(len(alphabet)) prediction=model.predict(x, verbose=0) index = np.argmax(prediction) result = index2char[index] seq_in=[index2char[value] for value in pattern] print(seq_in,"->", result) 有没有错,有错怎么改
answer:代码有一个拼写错误,在model.compile()函数中,loss应该为'categorical_crossentropy'而不是'categorical_crossentropx'。 更正后的代码如下: ```python import numpy as np from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from keras.utils import np_utils from tensorflow.keras.preprocessing.sequence import pad_sequences maxlen = 100 alphabet='hello' chars=set([char for char in alphabet]) chars_count=len(chars) char2index=dict((c,i) for i,c in enumerate(chars)) index2char=dict((i,c) for i,c in enumerate(chars)) seq_len=1 dataX=[] dataY=[] for i in range(0,len(alphabet)-seq_len ,1): seq_in=alphabet[0:i+ seq_len] seq_out=alphabet[i+ seq_len] dataX.append([char2index[ch] for ch in seq_in]) dataY.append(char2index[seq_out]) max_len=len(alphabet)-1. X = pad_sequences(dataX, maxlen=max_len, dtype='float32') X=np.reshape(X,(len(dataX),max_len,1)) print(X) X=X/float(chars_count) y=np_utils.to_categorical(dataY) model=Sequential() model.add(LSTM(32,input_shape=(X.shape[1],X.shape[2]),return_sequences=False,unroll=True)) model.add(Dense(y.shape[1],activation='softmax')) model.compile(metrics=['accuracy'],optimizer='adam',loss='categorical_crossentropy') model.fit(X,y,batch_size=1,nb_epoch=500,verbose=2) scores=model.evaluate(X,y,verbose=0) print('model accuacy:%0.2f%%'%(scores[1]*100)) for pattern in dataX: x=pad_sequences([pattern],maxlen=max_len,dtype='float32') x = np.reshape(x,(1, max_len, 1)) x=x / float(len(alphabet)) prediction=model.predict(x, verbose=0) index = np.argmax(prediction) result = index2char[index] seq_in=[index2char[value] for value in pattern] print(seq_in,"->", result) ```
question:a = empty(shape, dtype, order) ^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: 'float' object cannot be interpreted as an integer怎么改
answer:这个错误通常是因为尝试将浮点数作为整数使用,可以修复它将数据转换为整数类型。 修改你的代码如下: 假设这里出现问题的行是: ```python a = empty((m,n), dtype=float) ``` 将其修改为: ```python a = empty((m,n), dtype='float') ``` 或者直接将float类型修改为int类型: ```python a = empty((m,n), dtype=int) ``` 这样就可以解决这个错误了。