Pandas DataFrame 和 Keras [英] Pandas DataFrame and Keras

问题描述

我正在尝试使用 Keras 在 Python 中执行情绪分析.为此，我需要对我的文本进行词嵌入.当我尝试将数据拟合到我的模型时出现问题:

model_1 = Sequential()model_1.add(Embedding(1000,32, input_length = X_train.shape[0]))model_1.add(Flatten())model_1.add(Dense(250, activation='relu'))model_1.add(Dense(1, activation='sigmoid'))model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

我的火车数据的形状是

(4834,)

并且是 Pandas 系列对象.当我尝试拟合我的模型并使用其他一些数据对其进行验证时，我收到此错误:

model_1.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch_size=64,verbose=2)

<块引用>

ValueError:检查模型输入时出错:预期embedding_1_input 有形状(无，4834)但有形状的数组(4834, 1)

如何重塑我的数据以使其适合 Keras?我一直在尝试使用 np.reshape 但我无法使用该函数放置 None 元素.

提前致谢

解决方案

None 是进入训练的预期行数，因此您无法定义它.此外，Keras 需要一个 numpy 数组作为输入，而不是 Pandas 数据框.首先使用 df.values 将 df 转换为 numpy 数组，然后执行 np.reshape((-1, 4834)).请注意，您应该使用 np.float32.如果您在 GPU 上训练它，这一点很重要.

I'm trying to perform a sentiment analysis in Python using Keras. To do so, I need to do a word embedding of my texts. The problem appears when I try to fit the data to my model:

model_1 = Sequential()
model_1.add(Embedding(1000,32, input_length = X_train.shape[0]))
model_1.add(Flatten())
model_1.add(Dense(250, activation='relu'))
model_1.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

The shape of my train data is

(4834,)

And is a Pandas series object. When I try to fit my model and validate it with some other data I get this error:

model_1.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch_size=64, verbose=2)

ValueError: Error when checking model input: expected embedding_1_input to have shape (None, 4834) but got array with shape (4834, 1)

How can I reshape my data to make it suited for Keras? I've been trying with np.reshape but I cannot place None elements with that function.

Thanks in advance

解决方案

None is the number of expected rows that goes into training therefore you can't define it. Also Keras needs a numpy array as input and not a pandas dataframe. First convert the df to a numpy array with df.values and then do np.reshape((-1, 4834)). Note that you should use np.float32. This is important if you train it on GPU.

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