为什么我的Tensorflow CNN的精度为零,而损失却不是?

问题描述 投票:0回答:1

我正在尝试制作双CNN,我的数据库有两个最终合并在一起的输入和一个神经元作为IC50的输出。

当我尝试这样做时,我的损失为0,而我的准确度为0。我使用了错误的损失函数吗?当前是mean_squared_error

操作系统:Windows10tensorflow版本:2.3.0

我的代码“

encoded_drugs=np.load('encoded_drugs.npy')
encoded_cells=np.load('encoded_cells.npy')
encoded_ICs=np.load('encoded_ICs.npy')
encoded_drugs_train, encoded_drugs_test,encoded_cells_train, encoded_cells_test, encoded_ICs_train, encoded_ICs_test = train_test_split(encoded_drugs,encoded_cells, encoded_ICs, test_size=0.2)


input1=keras.layers.Input(shape=(139,32,))
x1=keras.layers.Flatten(input_shape=(139,32,))(input1)
x2=keras.layers.Dense(64,activation='relu')(x1)
x3=keras.layers.Dense(64,activation='relu')(x2)

input2=keras.layers.Input(shape=(735,2,))
y1=keras.layers.Flatten(input_shape=(735,2,))(input2)
y2=keras.layers.Dense(128,activation='relu')(y1)
y3=keras.layers.Dense(64,activation='relu')(y2)

merged=keras.layers.concatenate([x3,y3],axis=-1)

z=keras.layers.Dense(64,activation='relu')(merged)
out=keras.layers.Dense(1,activation='sigmoid')(z)

model=keras.models.Model(inputs=[input1,input2], outputs=out)

model.compile(optimizer='sgd',loss='mean_squared_error',metrics=['accuracy'])

model.fit([encoded_drugs_train,encoded_cells_train],encoded_ICs_train,validation_split = 0.2,epochs=2)

test_loss, test_accuracy= model.evaluate([encoded_drugs_test,encoded_cells_test],encoded_ICs_test)

print('Accuracy=', test_accuracy)

我的输出:

2020-02-18 11:06:00.759824: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations:  AVX AVX2
To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags.
2020-02-18 11:06:00.774869: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 4. Tune using inter_op_parallelism_threads for best performance.
Train on 75793 samples, validate on 18949 samples
Epoch 1/2
75793/75793 [==============================] - 17s 229us/sample - loss: 10.3671 - accuracy: 0.0000e+00 - val_loss: 10.4082 - val_accuracy: 0.0000e+00
Epoch 2/2
75793/75793 [==============================] - 11s 146us/sample - loss: 10.2673 - accuracy: 0.0000e+00 - val_loss: 10.3852 - val_accuracy: 0.0000e+00


3s 125us/sample - loss: 8.3239 - accuracy: 0.0000e+00
Accuracy= 0.0
python tensorflow machine-learning keras deep-learning
1个回答
0
投票

您正在尝试使用精度作为度量标准来解决回归问题(使用mean_squared_error损失)。在这种情况下,准确性不是有效的指标。

首先,请确保您要解决的问题确实是回归或分类问题。

在回归的情况下,使用Dense(1,activation='linear')作为最后一个输出层,并使用model.compile(optimizer='sgd',loss='mean_squared_error',metrics=['mse'])

在分类的情况下,将Dense(1,activation='sigmoid')用作最后一个输出层,并使用model.compile(optimizer='sgd',loss='binary_crossentropy',metrics=['accuracy'])

第二,您需要训练更多的纪元(29秒真的不足以对您的结果进行很好的概述)。

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