问训练3 3dconv神经网络失败，损失在.6931收敛

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# Gets the label of the image, the label determines how tensorflow will classify the image
def get_label(file_path):
    # Convert the path to a list of path components
    parts = tf.strings.split(file_path, os.path.sep)
    # The fourth last is the class-directory
    return float(parts[-4] == "class1")


# Reads the data from a .nii file and returns a NumPy ndarray that is compatible with tensorflow
def decode_img(img):
    img = nib.load(img.numpy().decode('utf-8'))
    # convert the compressed string to a NumPy ndarray
    data = img.get_fdata()
    # Resize img
    data = np.resize(data, imgshape)
    # Normalize
    max = np.amax(data)
    min = np.amin(data)
    data = ((data-min)/(max-min))
    return data


# Processes a path to return a image data and label pair
def process_path(file_path):
    # Gets the files label
    label = get_label(file_path)
    img = decode_img(file_path)
    return img, label

def configure_for_performance(ds):
    #ds = ds.cache(filename='cachefile')
    ds = ds.cache()
    ds = ds.shuffle(buffer_size=1000)
    ds = ds.repeat()
    ds = ds.batch(BATCH_SIZE)
    ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
    return ds

# Create a sequential network
model = tf.keras.Sequential([
    tf.keras.layers.Convolution3D(
        4, 4, padding='same', data_format="channels_last", input_shape=imgshape, activation='tanh'),
    tf.keras.layers.MaxPooling3D(padding='same'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Convolution3D(4, 4, padding='same', activation='tanh'),
    tf.keras.layers.MaxPooling3D(padding='same'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Convolution3D(4, 4, padding='same', activation='tanh'),
    tf.keras.layers.MaxPooling3D(padding='same'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Convolution3D(4, 4, padding='same', activation='tanh'),
    tf.keras.layers.MaxPooling3D(padding='same'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(2048, activation='tanh'),
    tf.keras.layers.Dense(1024, activation='tanh'),
    tf.keras.layers.Dense(512, activation='tanh'),
    tf.keras.layers.Dense(256, activation='tanh'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam',
              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
              metrics=['accuracy'])
model.summary()
model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=500,
    steps_per_epoch=BATCH_SIZE,
    validation_steps=BATCH_SIZE
)