培训和测试的准确性和损失没有变化

Question

我正在尝试使用CNN对3类数据进行分类，每个数据为30 * 188。 Class1有5794个数据，class2有8471，class3有9092。当我训练模型时，准确性，loss，val_acc和val_loss的值不变。 请帮我解决这个问题。

import glob
import os
import librosa
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import specgram
import librosa.display
import sklearn
from keras.utils import to_categorical
import scipy.io as scio

path1 = 'class1_feature_array.mat'
data1 = scio.loadmat(path1)
class1_feature_array = data1['class1_feature_array']
class1_label = np.zeros((class1_feature_array.shape[0],))
class1_label=class1_label.astype(np.int32)
class1_label=class1_label.astype(np.str)

path2 = 'class2_feature_array.mat'
data2 = scio.loadmat(path2)
class2_feature_array = data2['class2_feature_array']
class2_label = np.ones((class2_feature_array.shape[0],))
class2_label=class2_label.astype(np.int32)
class2_label=class2_label.astype(np.str)

path3 = 'class3_feature_array.mat'
data3 = scio.loadmat(path3)
class3_feature_array = data3['class3_feature_array']
class3_label = np.ones((class3_feature_array.shape[0],))*2
class3_label=class3_label.astype(np.int32)
class3_label=class3_label.astype(np.str)

features, labels = np.empty((0,40,188)), np.empty(0)
features = np.append(features,class1_feature_array,axis=0)
features = np.append(features,class2_feature_array,axis=0)
features = np.append(features,class3_feature_array,axis=0)
features = np.array(features)

labels = np.append(labels,class1_label,axis=0)
labels = np.append(labels,class2_label,axis=0)
labels = np.append(labels,class3_label,axis=0)
labels = np.array(labels, dtype = np.int)

def one_hot_encode(labels):
    n_labels = len(labels)
    n_unique_labels = len(np.unique(labels))
    one_hot_encode = np.zeros((n_labels,n_unique_labels))
    print("one_hot_encode",one_hot_encode.shape)
    one_hot_encode[np.arange(n_labels), labels] = 1
    return one_hot_encode

labels = one_hot_encode(labels)

train_test_split = np.random.rand(len(features)) < 0.80
train_x = features[train_test_split]
train_y = labels[train_test_split]
test_x = features[~train_test_split]
test_y = labels[~train_test_split]

train_x = train_x.reshape(train_x.shape[0],train_x.shape[1],train_x.shape[2],1)
test_x = test_x.reshape(test_x.shape[0],test_x.shape[1],test_x.shape[2],1)

import sklearn
import keras
from keras.models import Sequential
from keras.layers import *
from keras.callbacks import LearningRateScheduler
from keras import optimizers

#LeNet
model = Sequential()

model.add(Conv2D(32,(5, 5),strides=(1,1),padding='valid',activation='relu',input_shape=(40,188,1),kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64,(5,5),strides=(1,1),padding='valid',activation='relu',kernel_initializer='uniform'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
model.add(Dense(100,activation='relu'))
model.add(Dense(3, activation='softmax'))

sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
            loss='binary_crossentropy',
            metrics=['accuracy'])
model.summary(line_length=80)

history = model.fit(train_x, train_y, epochs=100, batch_size=32, validation_data=(test_x, test_y))

训练后的输出如下：

Train on 18625 samples, validate on 4732 samples

Epoch 1/100
18625/18625 [==============================] - 30s 2ms/step - loss: 8.0138 - accuracy: 0.5001 - val_loss: 8.1055 - val_accuracy: 0.4944

Epoch 2/100
18625/18625 [==============================] - 22s 1ms/step - loss: 8.0181 - accuracy: 0.4998 - val_loss: 8.1055 - val_accuracy: 0.4944

Epoch 3/100
18625/18625 [==============================] - 23s 1ms/step - loss: 8.0181 - accuracy: 0.4998 - val_loss: 8.1055 - val_accuracy: 0.4944

Epoch 4/100
18625/18625 [==============================] - 24s 1ms/step - loss: 8.0181 - accuracy: 0.4998 - val_loss: 8.1055 - val_accuracy: 0.4944

Epoch 5/100
18625/18625 [==============================] - 23s 1ms/step - loss: 8.0181 - accuracy: 0.4998 - val_loss: 8.1055 - val_accuracy: 0.4944

Epoch 6/100
18625/18625 [==============================] - 24s 1ms/step - loss: 8.0181 - accuracy: 0.4998 - val_loss: 8.1055 - val_accuracy: 0.4944

Epoch 7/100
18625/18625 [==============================] - 24s 1ms/step - loss: 8.0181 - accuracy: 0.4998 - val_loss: 8.1055 - val_accuracy: 0.4944

Epoch 8/100
18625/18625 [==============================] - 25s 1ms/step - loss: 8.0181 - accuracy: 0.4998 - val_loss: 8.1055 - val_accuracy: 0.4944

Epoch 9/100
18625/18625 [==============================] - 26s 1ms/step - loss: 8.0181 - accuracy: 0.4998 - val_loss: 8.1055 - val_accuracy: 0.4944

Epoch 10/100
18625/18625 [==============================] - 25s 1ms/step - loss: 8.0181 - accuracy: 0.4998 - val_loss: 8.1055 - val_accuracy: 0.4944

Epoch 11/100
18625/18625 [==============================] - 26s 1ms/step - loss: 8.0181 - accuracy: 0.4998 - val_loss: 8.1055 - val_accuracy: 0.4944

Epoch 12/100
18625/18625 [==============================] - 26s 1ms/step - loss: 8.0181 - accuracy: 0.4998 - val_loss: 8.1055 - val_accuracy: 0.4944