数据挖掘 - 为什么 VGG16 的训练精度是恒定的？ - 吾爱随笔录

我想使用 VGG16 训练一个模型，以按调制类型对无线电信号进行分类。类似于这篇论文（Over the Air Deep LearningBased Radio Signal Classification）所以我使用 Keras 从头开始构建模型，并将输入形状设置为 (2, 1024) 1024 个复杂点：

batch_size = 512
num_classes = 3
epochs = 100
img_rows, img_cols = 2, 1024
hf = h5py.File('ask.hdf5', 'r')

x = np.array(hf['X'][::])
y = np.array(hf['Y'][::])
if K.image_data_format() == 'channels_first':
    x = x.reshape(x.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x = x.reshape(x.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.15, shuffle=True)
x_training, x_val, y_training, y_val = train_test_split(x_train, y_train, test_size=0.15, shuffle=True)

model = Sequential()
model.add(Conv2D(64, kernel_size=(1, 3), activation='relu', padding='same', input_shape=input_shape))
model.add(Conv2D(64, (1, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(1, 2), strides=(1, 2)))
model.add(Conv2D(128, (1, 3), padding='same', activation='relu'))
model.add(Conv2D(128, (1, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(1, 2), strides=(1, 2)))
model.add(Conv2D(256, (1, 3), padding='same', activation='relu'))
model.add(Conv2D(256, (1, 3), padding='same', activation='relu'))
model.add(Conv2D(256, (1, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(1, 2), strides=(1, 2)))
model.add(Conv2D(512, (1, 3), padding='same', activation='relu'))
model.add(Conv2D(512, (1, 3), padding='same', activation='relu'))
model.add(Conv2D(512, (1, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(1, 2), strides=(1, 2)))
model.add(Conv2D(512, (1, 3), padding='same', activation='relu'))
model.add(Conv2D(512, (1, 3), padding='same', activation='relu'))
model.add(Conv2D(512, (1, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(1, 2), strides=(1, 2)))
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dense(4096, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy, optimizer='adam', metrics=['accuracy'])
callbacks = [keras.callbacks.ModelCheckpoint('model0c.h5', monitor='val_acc', verbose=0, save_best_only=True, mode='auto'), keras.callbacks.EarlyStopping(monitor='val_acc', patience=20, verbose=0, mode='auto')]
history = model.fit(x_training, y_training, batch_size=batch_size, epochs=epochs, verbose=2, validation_data=(x_val, y_val), callbacks=callbacks)

但是在训练过程中，我可以看到模型没有学习并且指标是恒定的。

Epoch 1/100
 - 153s - loss: 1.1004 - accuracy: 0.3348 - val_loss: 1.0991 - val_accuracy: 0.3236
Epoch 2/100
 - 153s - loss: 1.0988 - accuracy: 0.3374 - val_loss: 1.0987 - val_accuracy: 0.3236
Epoch 3/100
 - 148s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0986 - val_accuracy: 0.3236
Epoch 4/100
 - 147s - loss: 1.0986 - accuracy: 0.3298 - val_loss: 1.0988 - val_accuracy: 0.3236
Epoch 5/100
 - 147s - loss: 1.0987 - accuracy: 0.3374 - val_loss: 1.0987 - val_accuracy: 0.3236
Epoch 6/100
 - 147s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0989 - val_accuracy: 0.3236
Epoch 7/100
 - 147s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0986 - val_accuracy: 0.3236
Epoch 8/100
 - 148s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0989 - val_accuracy: 0.3236
Epoch 9/100
 - 148s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0987 - val_accuracy: 0.3236
Epoch 10/100
 - 149s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0988 - val_accuracy: 0.3236
Epoch 11/100
 - 148s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0987 - val_accuracy: 0.3236
Epoch 12/100
 - 148s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0987 - val_accuracy: 0.3236
Epoch 13/100
 - 148s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0987 - val_accuracy: 0.3236
Epoch 14/100
 - 147s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0988 - val_accuracy: 0.3236
Epoch 15/100
 - 148s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0988 - val_accuracy: 0.3236
Epoch 16/100
 - 146s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0986 - val_accuracy: 0.3236
Epoch 17/100
 - 149s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0987 - val_accuracy: 0.3236
Epoch 18/100
 - 147s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0987 - val_accuracy: 0.3236
Epoch 19/100
 - 147s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0989 - val_accuracy: 0.3236
Epoch 20/100
 - 147s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0988 - val_accuracy: 0.3236
Epoch 21/100
 - 147s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0988 - val_accuracy: 0.3236
Epoch 22/100
 - 147s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0988 - val_accuracy: 0.3236
Epoch 23/100
 - 146s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0988 - val_accuracy: 0.3236
Epoch 24/100
 - 148s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0988 - val_accuracy: 0.3236
Epoch 25/100
 - 147s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0988 - val_accuracy: 0.3236
Epoch 26/100
 - 147s - loss: 1.0986 - accuracy: 0.3374 - val_loss: 1.0987 - val_accuracy: 0.3236

可能是什么问题呢？