我们的val_loss和val_acc有一些问题。经过几个时期（约30个）后，val_acc下降了约50-60％，而val_loss则增加到了0.98-1.4之间（请参见下图）。该帖子的结尾是第45个时代的结束。

    import pickle
    from datetime import time
    import matplotlib.pyplot as plt
    import numpy as np
    import tf as tf
    from keras import optimizers
    from keras.models import Sequential
    from keras.layers import *
    from keras.callbacks import TensorBoard
    from keras.utils import np_utils

    pickle_in = open("X.pickle","rb")
    X = pickle.load(pickle_in)

    pickle_in = open("y.pickle","rb")
    y = pickle.load(pickle_in)

    pickle_in = open("PredictionData\\X_Test.pickle","rb")
    X_Test = pickle.load(pickle_in)

    X = X/255.0
    X_Test = X_Test/255.0

    y = np_utils.to_categorical(y,5)

    NAME = "Emotion Detection"

    model = Sequential()

    model.add(Conv2D(32,(1,1),activation="relu",use_bias=True,bias_initializer="Ones",input_shape=(145,65,dim_ordering="th"))

    model.add(Conv2D(64,(3,3),activation="relu"))

    model.add(Conv2D(128,activation="relu"))
    model.add(Dropout(0.2))

    model.add(Conv2D(64,activation="relu"))

    model.add(flatten())  # this converts our 3D feature maps to 1D feature vectors

    model.add(Dense(128,activation="relu"))
    model.add(Dropout(0.2))

    model.add(Dense(32,activation="relu"))

    model.add(Dense(5,activation='sigmoid'))

    tensorboard = TensorBoard(log_dir="Tensorboard\\".format(time))

    sgd = optimizers.SGD(lr=0.001,decay=1e-6,momentum=0.9,nesterov=True)

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

    history = model.fit(X,y,batch_size=16,epochs=45,validation_split=0.12,callbacks=[tensorboard])


    plt.plot(history.history['accuracy'])
    plt.plot(history.history['val_accuracy'])
    plt.title('Model accuracy')
    plt.ylabel('accuracy')
    plt.xlabel('Epoch')
    plt.legend(['accuracy','Val_accuracy'],loc='upper left')
    plt.show()

    plt.plot(history.history['loss'])
    plt.plot(history.history['val_loss'])
    plt.title('Model loss')
    plt.ylabel('Loss')
    plt.xlabel('Epoch')
    plt.legend(['Loss','Val_Loss'],loc='upper left')
    plt.show()

    classes = model.predict(X_Test)
    plt.bar(range(5),classes[0])
    plt.show()
    print("prediction: class",np.argmax(classes[0]))


    model.summary()

    model.save("Trainedmodel\\emotionDetector.h5") 

2493/2493 [==============================]-35s 14ms / step-损耗：0.2324-精度： 0.9202-val_loss：1.3789-val_accuracy：0.6353

_________________________________________________________________
Layer (type)                 Output Shape              Param    
=================================================================
conv2d_1 (Conv2D)            (None,32,1)         4672      
_________________________________________________________________
conv2d_2 (Conv2D)            (None,30,63,64)        640       
_________________________________________________________________
conv2d_3 (Conv2D)            (None,28,61,128)       73856     
_________________________________________________________________
dropout_1 (Dropout)          (None,128)       0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None,26,59,64)        73792     
_________________________________________________________________
flatten_1 (flatten)          (None,98176)             0         
_________________________________________________________________
dense_1 (Dense)              (None,128)               12566656  
_________________________________________________________________
dropout_2 (Dropout)          (None,128)               0         
_________________________________________________________________
dense_2 (Dense)              (None,32)                4128      
_________________________________________________________________
dense_3 (Dense)              (None,5)                 165       
_________________________________________________________________
Total params: 12,723,909
Trainable params: 12,909
Non-trainable params: 0
_________________________________________________________________

希望您能为我们提供帮助。预先感谢。