[英]fit deep learning model using keras
我是深度学习和 keras 的新手,我想做一个任务:使用 50 个时期在训练数据上训练模型。
我写了这个代码:
import pandas as pd
from tensorflow.python.keras import Sequential
from tensorflow.python.keras.layers import Dense
from sklearn.model_selection import train_test_split
concrete_data = pd.read_csv('https://cocl.us/concrete_data')
n_cols = concrete_data.shape[1]
model = Sequential()
model.add(Dense(units=10, activation='relu', input_shape=(n_cols,)))
model.compile(loss='mean_squared_error',
optimizer='adam')
x = concrete_data.Cement
y = concrete_data.drop('Cement', axis=1)
xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.3)
但是当我想以这种方式拟合我的模型时:
model.fit(xTrain, yTrain, validation_data=(xTrain, yTrain), epochs=50)
我有这个错误:
Epoch 1/50
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-83-489dd99522b4> in <module>()
----> 1 model.fit(xTrain, yTrain, validation_data=(xTrain, yTrain), epochs=50)
10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
966 except Exception as e: # pylint:disable=broad-except
967 if hasattr(e, "ag_error_metadata"):
--> 968 raise e.ag_error_metadata.to_exception(e)
969 else:
970 raise
ValueError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:503 train_function *
outputs = self.distribute_strategy.run(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 run **
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:464 train_step **
y_pred = self(x, training=True)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:885 __call__
self.name)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py:216 assert_input_compatibility
' but received input with shape ' + str(shape))
ValueError: Input 0 of layer sequential_2 is incompatible with the layer: expected axis -1 of input shape to have value 9 but received input with shape [None, 1]
我认为你需要改变 input_shape 如下:
input_shape=(n_cols,) =>> input_shape=(n_cols-1,)
一开始,您的数据包括特征和目标数据,因此形状由两者组成。 您需要从该部分减去 1 以指定输入形状。
另一个问题是您需要在x
和y
之间切换数据。 我认为你想用你的数据集的其余部分来预测Cement
。 因此, Cement
信息应存储在y
,其余数据集应存储在x
。
此外,您需要更改这部分代码。
model.fit(xTrain, yTrain, validation_data=(xTrain, yTrain), epochs=50)
在训练和验证中使用相同的数据没有意义。 您可以指定验证比率,以便 keras 自动生成您。
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