Question

当我使用Keras估计器的预测函数时，出现了一个小块形状错误。我使用以下代码构建，评估然后重新训练模型：

import pandas as pd
import sqlalchemy as sqla
import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import Pipeline

# Connect to to the DB and retrieve the iris table
con = sqla.create_engine('postgresql://tristan:sebens@db:5432/tristan')
con.connect()
table_name = "iris"
schema = "public"
iris = pd.read_sql_table(table_name, con, schema=schema)

iris.head()

iris_ds = iris.values # Convert the table to a numpy array
X = iris_ds[:, 0:4].astype(float) # Slice the descriptive features into a numpy array
Y = iris_ds[:, 4] # Slice the labels away as their own numpy array

# The labels are encoded as strings, so we need to encode them 
# as numbers that can be output by an ANN
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# convert integers to dummy variables (i.e. one hot encoded)
dummy_y = to_categorical(encoded_Y)

# define baseline model
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(8, input_dim=4, activation='relu'))
    model.add(Dense(3, activation='softmax'))
    # Compile model
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

seed = 7

# Train the model:

# First we define the model as a classifier. This will affect the process used to train it 
estimator = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=5, verbose=0)
# Honestly not totally sure what this is, but it has to do with splitting the training/evaluation data in 
#  a way that gives us a more realistic metric of the model's accuracy
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
# Now that we have our classifier and our data pipeline defined, we can begin the training process
results = cross_val_score(estimator, X, dummy_y, cv=kfold)
print("Baseline: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))

# If we like our accuracy, then we can train the model for real
# Evaluating the model actually evaluates a clone of the model, so now we need to train the model again
estimator.fit(X, dummy_y)

这就是麻烦所在。我尝试做出测试预测：

# Let's make a test prediction with our model
x = X[0]
estimator.predict(x)

出现输入形状错误：

ValueError: Error when checking input: expected dense_21_input to have shape (4,) but got array with shape (1,)

我很茫然。如果输入实际上是训练数据集的成员，怎么会有错误的形状？

Keras估算器预测说输入的格式错误

0 个答案: