无法将 numpy 数组加载到“model.fit”中

Question

我是 Keras 深度学习的新手，所以如果我需要在这篇文章中包含更多数据，请通知我！所以目前我已经对我拥有的 MNIST 数据集的训练集进行了一些图像增强。所以，我参考了这篇文章here，并尝试将我的增强图像模型保存到数组中。但是当我尝试重新加载图像并放入我的mode.fit() 时，它会抛出这个错误：

AttributeError: 'NoneType' object has no attribute 'shape'

我该如何解决？

这是我当前的代码：

# get the dataset from keras library in tensorflow 2.0
mnist = tf.keras.datasets.mnist

# unpack the dataset to the respective x_train, y_train, x_test and y_test
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# flatten 28*28 pixel images to 784 pixels for each image(from a 2D array to a 1D array)
num_pixels = x_train.shape[1] * x_train.shape[2]
X_train = x_train.reshape(x_train.shape[0], num_pixels).astype('float32')
X_test = x_test.reshape(x_test.shape[0], num_pixels).astype('float32')

# standardise X_train and X_test
X_train /= 255
X_test /= 255

# convert to categorical 
Y_train = tf.keras.utils.to_categorical(y_train, num_classes)
Y_test = tf.keras.utils.to_categorical(y_test, num_classes)


# reshape x_train and x_test to (n_images, x_shape, y_shape, channels)
# we are going to make chanels be 1 as we are not dealing with rgb images.
X_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
X_test = x_test.reshape(x_test.shape[0], 28, 28, 1)

#  my cnn model
# Instantiate a Sequential model
model = Sequential(name="cnn_model_hyperParamTuned_sequential_LeNet")

# Layer 1 Conv2D
model.add(Conv2D(filters=150, kernel_size=(5, 5), strides=(1, 1), activation='elu', input_shape=(28, 28, 1), padding='same'))
model.add(BatchNormalization())
# Layer 2 Pooling Layer
model.add(AveragePooling2D(pool_size=(2, 2), strides=(2, 2)))

# Layer 3 Conv2D
model.add(Conv2D(filters=140, kernel_size=(5, 5), strides=(1, 1), activation='elu', padding='valid'))
model.add(BatchNormalization())
# Layer 4 Pooling Layer
model.add(AveragePooling2D(pool_size=(2, 2), strides=(2, 2)))

# Layer 5 Flatten
model.add(Flatten())

# Layer 6 Fully Connected Layer (Hidden Layer)
model.add(Dense(units=120, activation='tanh', kernel_initializer='normal', kernel_regularizer=l2(0.0001)))
model.add(Dropout(0.2))

# # Layer 7 Fully Connected Layer (Hidden Layer)
# model.add(Dense(units=84, activation='softmax'))

# Output layer
model.add(Dense(units=num_classes, activation='softmax'))

model.compile(optimizer='sgd',
          loss='categorical_crossentropy',
          metrics=['accuracy'])
# declare decay_rate and learning_rate here
learning_rate = 0.1 
decay_rate = 0.1

# define the learning rate change 
def exp_decay(epoch):
    lrate = learning_rate * np.exp(-decay_rate*epoch)
    return lrate
    
# learning schedule callback
loss_history = History()
lr_rate = LearningRateScheduler(exp_decay)
callbacks_list = [lr_rate]

# we shall do some ImageDataGenerator here to augment the input data, which can help prevent over-fitting
train_gen = ImageDataGenerator(
    rotation_range=8,
    shear_range=0.01,
    zoom_range=0.08,
    width_shift_range=0.1,
    height_shift_range=0.1,
    zca_whitening=True
)
test_gen =  ImageDataGenerator()

# we want to store the augmented data in a numpy array so the next time we run this we do not get a different set of images, which might result in a totally different loss score
augmented_data = []
num_augmented = 0

for X_batch, Y_batch in train_gen.flow(X_train, Y_train, batch_size=100):
  augmented_data.append(X_batch)
  num_augmented += 100
  if num_augmented == X_train.shape[0]:
    break

# concatenate the augmented data into a numpy array
augmented_data = np.concatenate(augmented_data)

# create a "flow" for the data to flow through
# train_generator = train_gen.flow(X_train, Y_train, batch_size=100)
test_generator = test_gen.flow(X_test, Y_test, batch_size=100)

# train the model(we use .fit since .fit_generator is depreciated already)
history = model.fit(augmented_data,
                    batch_size=640,
                    steps_per_epoch=60000//100,
                    epochs=60,
                    callbacks=[EarlyStopping(monitor='val_loss', patience=5), callbacks_list],
                    validation_data=test_generator,
                    validation_steps=10000//100,
                    verbose=1)

Answer 1

如果您使用 zca_whitening=True 则有必要在训练之前使生成器适合数据，因此请使用

train_gen.fit(X_train)

我把你的代码运行到了

# concatenate the augmented data into a numpy array
augmented_data = np.concatenate(augmented_data)

我打印了看起来正确的增强数据的形状，形状为 (60000, 28, 28, 1) 我没有进一步进行，因此无法找到问题，但至少数据进入了 model.fit看起来正确。我的模型代码有问题。