Keras fit_generator（）由于形状错误而无法工作

Question

我正在使用Keras和tensorflow后端运行MNIST预测。 我有使用Keras fit（） as

批处理运行的代码

(X_train, y_train), (X_test, y_test) = mnist.load_data()

N1 = X_train.shape[0]  
N2 = X_test.shape[0]  
h = X_train.shape[1]
w = X_train.shape[2]

num_pixels = h*w
# reshape N1 samples to num_pixels
x_train = X_train.reshape(N1, num_pixels).astype('float32') # shape is now (60000,784)
x_test = X_test.reshape(N2, num_pixels).astype('float32') # shape is now (10000,784)

x_train = x_train / 255
x_test = x_test / 255

y_train = np_utils.to_categorical(y_train) #(60000,10)
y_test = np_utils.to_categorical(y_test) # (10000,10): 
num_classes = y_test.shape[1]


def baseline_model():
# create model
    model = Sequential()
    model.add(Dense(num_pixels, input_dim=num_pixels, kernel_initializer='normal', activation='relu'))    
    model.add(Dense(num_classes, kernel_initializer='normal', activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model
model = baseline_model()
batch_size = 200
epochs = 20
max_batches = 2 * len(x_train) / batch_size # 2*60000/200

# reshape to be [samples][width][height][ channel] for ImageDataGenerator
x_t = X_train.reshape(N1, w, h, 1).astype('float32')
datagen = ImageDataGenerator(rescale= 1./255)
train_gen = datagen.flow(x_t, y_train, batch_size=batch_size)
for e in range(epochs):
    batches = 0
    for x_batch, y_batch in train_gen:
    # x_batch is of size [batch_sz,w,h,ch]: resize to [bth_sz,pixel_sz]: (200,28,28,1)-> (200,784)
    # for model.fit
        x_batch = np.reshape(x_batch, [-1, num_pixels])            
        model.fit(x_batch, y_batch,validation_split=0.15,verbose=0)
        batches += 1
        print("Epoch %d/%d, Batch %d/%d" % (e+1, epochs, batches, max_batches))
        if batches >= max_batches:
            break

scores = model.evaluate(x_test, y_test, verbose=0)

但是，当我尝试使用 fit_generator（）实现类似代码时，出现错误。 代码如下：

(X_train, y_train), (X_test, y_test) = mnist.load_data()
# separate data into train and validation
from sklearn.model_selection import train_test_split
# Split the data
X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.15, shuffle= True)

# number of training samples
N1 = X_train.shape[0]  # training size
N2 = X_test.shape[0]   # test size
N3 = X_valid.shape[0]  # valid size
h = X_train.shape[1]
w = X_train.shape[2]

num_pixels = h*w

y_train = np_utils.to_categorical(y_train) 
y_valid = np_utils.to_categorical(y_valid)
y_test = np_utils.to_categorical(y_test) 

num_classes = y_test.shape[1]

def baseline_model():
# create model
    model = Sequential()
    model.add(Dense(num_pixels, input_dim=num_pixels, kernel_initializer='normal', activation='relu'))   
    model.add(Dense(num_classes, kernel_initializer='normal', activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

model = baseline_model()

batch_size = 200
epochs = 20
steps_per_epoch_tr = int(N1/ batch_size) # 51000/200
steps_per_epoch_val =  int(N3/batch_size) 

# reshape to be [samples][width][height][ channel] for ImageData Gnerator->datagen.flow
x_t = X_train.reshape(N1, w, h, 1).astype('float32')
x_v = X_valid.reshape(N3, w, h, 1).astype('float32')

# define data preparation
datagen = ImageDataGenerator(rescale=1./255) # scales x_t/x_v
train_gen = datagen.flow(x_t, y_train, batch_size=batch_size)
valid_gen = datagen.flow(x_v,y_valid, batch_size=batch_size)



model.fit_generator(train_gen,steps_per_epoch = steps_per_epoch_tr,validation_data = valid_gen,
                    validation_steps = steps_per_epoch_val,epochs=epochs)

这给出了一个错误：

这是由于预期的图像尺寸错误引起的，但是我不确定在哪里/如何解决此问题。任何帮助将不胜感激。
谢谢
赛迪

Answer 1

在model.fit（）情况下，此行在输入用于训练之前将其弄平。

x_batch = np.reshape（x_batch，[-1，num_pixels]）

但是在生成器的情况下，在将输入馈送到“密集”层之前，没有什么可以使输入变平。密集层无法处理2D输入（28 x 28）。在模型中添加Flatten（）层可以达到以下目的。

def baseline_model():

    # create model
        model = Sequential()
        model.add(Flatten(input_shape=(28,28,1)))
        model.add(Dense(num_pixels, input_dim=num_pixels, kernel_initializer='normal', activation='relu'))   
        model.add(Dense(num_classes, kernel_initializer='normal', activation='softmax'))
        model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
        return model