machine learing

import json

import torch

from sklearn import preprocessing
from sklearn.model_selection import train_test_split

from tensorflow import keras
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.optimizers import Adam
# from keras import losses
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten

import numpy as np

import os

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"


JSON_SET_PATH = "json_set/data.json"
GENRE_LIST = ['blues', 'classical', 'country', 'disco', 'hiphop', 'jazz', 'metal', 'pop', 'reggae', 'rock']


def load_data(json_set_path):
    # X, y = [], []
    with open(json_set_path, "r", encoding="utf-8") as json_data:
        data = json.loads(json_data.read())

    Label = preprocessing.LabelEncoder()
    Label.fit(GENRE_LIST)

    y = np.array(Label.transform(data["label"]), dtype=float)
    X = data["mfcc"]

    # X = np.array(data["mfcc"], dtype=float)
    # print(numpy.array(X).shape)
    # print(len(X[]), len(X[0]))

    for i in data["mfcc"]:
        for j in i:
            if len(j) != 647:
                if len(j) > 647:
                    del j[647:]
                else:
                    for num in range(0, 647-len(j)):
                        j.append(0)
                    # print("index:", data["mfcc"].index(i), "after append:", len(j))
            else:
                pass

    # print("X.shape", np.array(X).shape, "y.shape", np.array(y).shape)
    # X.shape (1000, 20, 647) y.shape (1000,)
    return np.array(X), np.array(y)


def define_model(input_shape=(20, 647)):
    model = Sequential()
    model.add(Conv2D(input_shape=input_shape, filters=64, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=64, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(filters=128, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=128, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))

    model.add(Flatten())
    model.add(Dense(units=4096, activation="relu"))
    model.add(Dense(units=4096, activation="relu"))
    model.add(Dense(units=2, activation="softmax"))

    # show the model information

    # model.summary()
    return model


def main():
    # load the data
    X, y = load_data(json_set_path=JSON_SET_PATH)

    # ndarray 转换为tensor张量
    X_tensor = torch.from_numpy(X)
    y_tensor = torch.from_numpy(y)
    # split the data into the train adn test
    X_train, X_test, y_train, y_test = train_test_split(X_tensor, y_tensor, test_size=0.25)
    # X_train.shape torch.Size([750, 20, 647]) y_train_shape torch.Size([750])
    # X_train type: <class 'torch.Tensor'> y_train type: <class 'torch.Tensor'>

    # load the model
    model = define_model()
    opt = Adam(learning_rate=0.001)

    model.compile(optimizer=opt, loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])
    model.fit(x=X_train, y=y_train, batch_size=None, epochs=20, verbose=1, validation_data=(X_test, y_test))

    checkpoint = ModelCheckpoint("vgg16_1.h5",
                                 monitor='val_acc',
                                 verbose=1,
                                 save_best_only=True,
                                 save_weights_only=False,
                                 mode='auto', period=1)
    early = EarlyStopping(monitor='val_acc',
                          min_delta=0,
                          patience=20,
                          verbose=1,
                          mode='auto')
    hist = model.fit_generator(steps_per_epoch=100,
                               generator=X_train,
                               validation_data=X_test,
                               validation_steps=10,
                               epochs=100,
                               callbacks=[checkpoint, early])


if __name__ == "__main__":
    main()

ERROR_INFORMATION:
2022-05-13 19:56:53.435198: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Traceback (most recent call last):
  File "C:/Users/xxxx/Pycharm Projects/Graduation_Project/VGG 16.py", line 132, in <module>
    main()
  File "C:/Users/xxxx/Pycharm Projects/Graduation_Project/VGG 16.py", line 106, in main
    model = define_model()
  File "C:/Users/xxxx/Pycharm Projects/Graduation_Project/VGG 16.py", line 62, in define_model
    model.add(Conv2D(input_shape=input_shape, filters=64, kernel_size=(3, 3), padding="same", activation="relu"))
  File "C:\Users\xxxx\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\training\tracking\base.py", line 629, in _method_wrapper
    result = method(self, *args, **kwargs)
  File "C:\Program Files\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "C:\Program Files\Python37\lib\site-packages\keras\engine\input_spec.py", line 228, in assert_input_compatibility
    raise ValueError(f'Input {input_index} of layer "{layer_name}" '
ValueError: Input 0 of layer "conv2d" is incompatible with the layer: expected min_ndim=4, found ndim=3. Full shape received: (None, 20, 647)