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[英]ValueError: Input 0 of layer "max_pooling1d" is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: (None, 51644, 29, 32)
[英]ValueError: Input 0 of layer max_pooling1d is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: (None, 128, 1, 32)
我正在尝试创建一个 CNN model。 x_train 数据的形状为 (8040, 128),y_train 数据的形状为 (8040, 1)。 类似地,x_test 数据的形状为 (3960, 128),y_test 数据的形状为 (3960, 1)。
加载数据:
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(data,out_data,test_size=0.33, random_state=21)
print(x_train.shape,y_train.shape)
print(x_test.shape,y_test.shape)
#(8040, 128) (8040, 1)
#(3960, 128) (3960, 1)
from sklearn.preprocessing import OneHotEncoder
OHE = OneHotEncoder()
y_train1 = OHE.fit_transform(y_train).toarray()
y_test1 = OHE.fit_transform(y_test).toarray()
我在 CNN model 的输入层出现错误。 这是我的 model:
# Conv1:
model.add(keras.layers.Conv1D(filters = 32, kernel_size=1,strides = 1, activation='relu', input_shape=(128,1,1)))
# Conv2:
model.add(keras.layers.Conv1D(filters = 32, kernel_size=1,strides = 1, activation='relu'))
# Pool1:
model.add(keras.layers.MaxPool1D(pool_size=(3), strides = 1))
# BN1:
model.add(keras.layers.BatchNormalization())
# Conv3:
model.add(keras.layers.Conv1D(filters = 64, kernel_size=1,strides = 1, activation='relu'))
# Conv4:
model.add(keras.layers.Conv1D(filters = 64, kernel_size=1,strides = 1, activation='relu'))
# Pool2:
model.add(keras.layers.MaxPool1D(pool_size=(3), strides = 1))
# BN2:
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Conv1D(filters = 128, kernel_size=1,strides = 1, activation='relu'))
# Conv6:
model.add(keras.layers.Conv1D(filters = 128, kernel_size=1,strides = 1, activation='relu'))
# Pool3:
model.add(keras.layers.GlobalMaxPooling1D())
# BN4:
model.add(keras.layers.BatchNormalization())
import tensorflow as tf
model.add(tf.keras.layers.Flatten())
# Dense1:
model.add(keras.layers.Dense(units=256, activation='relu',use_bias=True))
# Dense2:
model.add(keras.layers.Dense(units=128, activation='relu',use_bias=True))
# BN3:
model.add(keras.layers.BatchNormalization())
# Dense3:
model.add(keras.layers.Dense(units=16, activation='softmax',use_bias=True))
from keras.utils import np_utils
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(x_train)
model_history = model.fit(scaler.transform(x_train),y_train1, batch_size=32, epochs=43, callbacks=[callback], verbose=1)
显示的错误是: ValueError: Input 0 of layer max_pooling1d is in compatible with the layer: expected ndim=3, found ndim=4. 收到的完整形状:(无、128、1、32)
我该如何解决这个问题? cnn层的参数应该是什么?
将您的input_shape
更改为
input_shape =(128,1)
或使用batch_input_shape
代替input_shape
并设置为
batch_input_shape=(128, 1, 1)
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