问如何将数组形式的model.get_weight保存到csv中？

EN

# fix random seed for reproducibility (split training and validation set)
seed = 7
np.random.seed(seed)

# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# reshape to be [samples][pixels][width][height]
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32')

# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255

# one hot encode outputs (label encoding)
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]

def tempsigmoid(x, temp=0.5):
    return K.sigmoid(x/temp)


def baseline_model():
    # create model
    model = Sequential()
    model.add(Conv2D(32, (5, 5), input_shape=(1, 28, 28), activation='relu'))
    #model.add(Dense, input_shape = (1,28,28), Activation='relu')
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(100, activation='relu'))
    model.add(Dense(num_classes, activation=tempsigmoid))

    # Compile model
    model.compile(loss='mae', optimizer=SGD(lr=0.1), metrics=['accuracy'])
    return model

    # build the model
model = baseline_model()

earlystopper = EarlyStopping(monitor='val_loss', min_delta=0.1, patience=0, verbose=2, mode='auto')

# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=200, verbose=2, callbacks=[earlystopper])

# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("CNN Error: %.2f%%" % (100-scores[1]*100))
# print("Metrics(Test loss & Test Accuracy): ")
print(scores)

weight = model.get_weights()
print(weight)

# to save weight after output
model.save_weights('Result/w_output.h5')