无法使用 numpy 数组训练模型

Question

我正在用浮点数训练以下自动编码器。

input_img = Input(shape=(2623,1), name='input')

x = ZeroPadding1D(1)(input_img)
x = Conv1D(32, 3, activation='relu', padding='same', use_bias=False)(input_img)
x = BatchNormalization(axis=-1)(x)
x = MaxPooling1D(2, padding='same')(x)
x = Conv1D(16, 3, activation='relu', padding='same', use_bias=False)(x)
x = BatchNormalization(axis=-1)(x)
x = MaxPooling1D(2, padding='same')(x)
x = Conv1D(16,3, activation='relu', padding='same', use_bias=False)(x)
x = BatchNormalization(axis=-1)(x)
encoded = MaxPooling1D(2, padding='same')(x)


x = Conv1D(16,3, activation='relu', padding='same', use_bias=False)(encoded)
x = BatchNormalization(axis=-1)(x)
x = UpSampling1D(2)(x)
x = Conv1D(16,3, activation='relu', padding='same', use_bias=False)(x)
x = BatchNormalization(axis=-1)(x)
x = UpSampling1D(2)(x)
x = Conv1D(32, 3, activation='relu', padding='same', use_bias=False)(x)  #input_shape=(30, 1))
x = BatchNormalization(axis=-1)(x)  
x = UpSampling1D(2)(x)
x = Cropping1D(cropping=(0, 1))(x) #Crop nothing from input but crop 1 element from the end
decoded = Conv1D(1, 3, activation='sigmoid', padding='same', use_bias=False)(x)  

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='rmsprop', loss='binary_crossentropy')


x = Input(shape=(16, 300), name="input")
h = x
h = Conv1D(filters=300, kernel_size=16,
             activation="relu", padding='same', name='Conv1')(h)
h = MaxPooling1D(pool_size=16, name='Maxpool1')(h)

我必须将数据转换为 numpy 数组才能对其进行处理，但是当模型开始训练时，我得到：

ValueError: could not convert string to float:

发生这种情况是因为我的训练数据如下：

train[0][1] # one number of training data

array(['0.001758873'], dtype=object)

我该怎么做才能避免训练数据中出现 "dtype=object"，或者我是否必须将其转换为其他数据？谢谢！

Answer 1

或许，作为预处理步骤，您可以使用类似以下内容将对象类型数组类型转换为浮点数组：

# if float32 is the desired & appropriate datatype
train = train.astype(numpy.float32)

无法使用 numpy 数组训练模型

Cannot train a model with numpy array

python

numpy

neural-network

autoencoder

deep-learning