重塑训练和测试数据

问题描述

我是编程新手。我正在尝试使用一维 CNN 基于两个特征（长度、交通量）对两个类别（碰撞、非碰撞）进行分类。当我尝试训练以下模型时，

# Training and Testing Data
X_train,y_train = train[['Traffic_Volume','length']].values,train['Crash'].values
X_test,y_test = SH[['Traffic_Volume',SH['Crash'].values
print ('Training data shape : ',X_train.shape,y_train.shape)
print ('Testing data shape : ',X_test.shape,y_test.shape)
# Training data shape :  (316,2) (316,)
# Testing data shape :  (343,2) (343,)

# Fit and Evaluate a Model
def baseline_model(n_features=343,seed=100):
    numpy.random.seed(seed)
    # set_random_seed(seed)
    tensorflow.random.set_seed(seed)
    # create model
    model = Sequential()
    model.add(Conv1D(32,3,padding = "same",input_shape=(343,2)))
    model.add(Activation('relu'))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(128,activation='relu'))
    model.add(Dropout(0.2))#
    model.add(Dense(64,activation='relu'))#
    model.add(Dense(2))
    model.add(Activation('softmax'))
    # Compile model
    numpy.random.seed(seed)
    tensorflow.random.set_seed(seed)
    model.compile(loss='categorical_crossentropy',optimizer='adagrad',metrics=['accuracy'])
   
    print (model.summary())
    return model

# Classification

n_features=2   
n_classes=2
batch_size=10
from multi_adaboost_CNN import AdaBoostClassifier as Ada_CNN
n_estimators =10
epochs =1
bdt_real_test_CNN = Ada_CNN(
    base_estimator=baseline_model(n_features=n_features),n_estimators=n_estimators,learning_rate=1,epochs=epochs)

bdt_real_test_CNN.fit(X_train,y_train,batch_size)
y_pred_CNN = bdt_real_test_CNN.predict(X_train)
print('\n Training accuracy of bdt_real_test_CNN (AdaBoost+CNN): {}'.format(accuracy_score(bdt_real_test_CNN.predict(X_train),y_train)))

我发现了这个 ValueError：

ValueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=3,found ndim=2. Full shape received: (None,2)

我想知道我应该改变什么才能获得高效的模型（Data.shape、n_features、n_classes 等）？

解决方法

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classification python reshape shapes