keras history NoneType

这个错误通常是因为在训练模型时没有将历史记录存储在变量中。在Keras中，使用fit()方法来训练模型，并将返回一个包含训练过程中损失和指标的历史记录对象。确保在调用fit()方法时，将其存储在一个变量中，例如：

history = model.fit(x_train, y_train, epochs=10, validation_data=(x_val, y_val))

如果在调用fit()方法时没有将历史记录存储在变量中，则会返回NoneType错误。

相关问题

写一维胶囊网络的代码keras并训练

好的，以下是一维胶囊网络的Keras代码及其训练过程：

# 导入必要的库
import keras
from keras import layers, models

# 定义一维胶囊网络
class Capsule(layers.Layer):
    def __init__(self, num_capsules, dim_capsule, routings=3, **kwargs):
        super(Capsule, self).__init__(**kwargs)
        self.num_capsules = num_capsules
        self.dim_capsule = dim_capsule
        self.routings = routings

    def build(self, input_shape):
        self.input_num_capsules = input_shape[1]
        self.input_dim_capsule = input_shape[2]

        # 初始化W_ij和b_i
        self.W = self.add_weight(shape=[self.input_num_capsules, self.num_capsules, self.input_dim_capsule, self.dim_capsule], initializer='glorot_uniform', name='W')
        self.b = self.add_weight(shape=[1, self.input_num_capsules, self.num_capsules, 1, 1], initializer='zeros', name='b')

        super(Capsule, self).build(input_shape)

    def call(self, inputs, training=None):
        inputs_expand = keras.backend.expand_dims(inputs, 2)

        inputs_tiled = keras.backend.tile(inputs_expand, [1, 1, self.num_capsules, 1, 1])

        inputs_hat = keras.backend.map_fn(lambda x: keras.backend.batch_dot(x, self.W, [3, 2]), elems=inputs_tiled)

        for i in range(self.routings):
            c = keras.backend.softmax(self.b, axis=2)

            outputs = keras.backend.batch_dot(c, inputs_hat, [2, 3])

            if i != self.routings - 1:
                outputs = keras.backend.map_fn(lambda x: keras.backend.l2_normalize(x, axis=-1), elems=outputs)

                b += keras.backend.batch_dot(outputs, inputs_hat, [3, 4])

        return keras.backend.reshape(outputs, [-1, self.num_capsules, self.dim_capsule])

    def compute_output_shape(self, input_shape):
        return tuple([None, self.num_capsules, self.dim_capsule])


# 定义模型
def CapsNet(input_shape, n_class, num_capsules, dim_capsule, routings):
    x = layers.Input(shape=input_shape)

    conv1 = layers.Conv1D(filters=256, kernel_size=9, strides=1, padding='valid', activation='relu', name='conv1')(x)

    primary_caps = layers.Conv1D(filters=num_capsules*dim_capsule, kernel_size=9, strides=2, padding='valid', name='primary_caps')(conv1)

    primary_caps_reshaped = layers.Reshape(target_shape=[-1, dim_capsule], name='primary_caps_reshaped')(primary_caps)

    digit_caps = Capsule(num_capsules=num_capsules, dim_capsule=dim_capsule, routings=routings, name='digit_caps')(primary_caps_reshaped)

    out_caps = layers.Lambda(lambda x: keras.backend.sqrt(keras.backend.sum(keras.backend.square(x), 2)), name='out_caps')(digit_caps)

    y = layers.Input(shape=(n_class,))

    masked = layers.Multiply(name='masked')([out_caps, y])

    masked_out = layers.Dense(units=512, activation='relu', name='masked_out')(masked)

    model = models.Model(inputs=[x, y], outputs=masked_out)

    return model


# 训练模型
def train(model, data, epoch_size_frac=1.0, epochs=1, batch_size=128):
    (X_train, Y_train), (X_test, Y_test) = data

    model.compile(optimizer=keras.optimizers.Adam(lr=0.001), loss='mse', metrics=['accuracy'])

    history = model.fit([X_train, Y_train], Y_train, batch_size=batch_size, epochs=epochs, validation_data=[[X_test, Y_test], Y_test])

    return history

以上是一维胶囊网络的Keras代码，接下来给出一个训练样例：

# 加载数据
(X_train, Y_train), (X_test, Y_test) = keras.datasets.mnist.load_data()

# 数据预处理
X_train = X_train.astype('float32') / 255.0
X_test = X_test.astype('float32') / 255.0
Y_train = keras.utils.to_categorical(Y_train.astype('float32'))
Y_test = keras.utils.to_categorical(Y_test.astype('float32'))

# 定义模型参数
input_shape = (X_train.shape[1], 1)
n_class = Y_train.shape[1]
num_capsules = 10
dim_capsule = 16
routings = 3

# 构建模型
model = CapsNet(input_shape, n_class, num_capsules, dim_capsule, routings)

# 训练模型
history = train(model=model, data=((X_train, Y_train), (X_test, Y_test)), epochs=10)

# 打印训练过程中的准确率和损失
print(history.history['accuracy'])
print(history.history['loss'])

以上就是一维胶囊网络的Keras代码及其训练过程。

keras实现resnet对四种天气分类，并绘制图像

好的，我可以为您提供大致的代码实现，但由于我无法进行代码编写和图像绘制，因此以下代码仅供参考。

首先，需要导入必要的库和模块：

import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.models import Model
from keras.layers import Input, Conv2D, BatchNormalization, Activation, MaxPooling2D, Dense, Flatten, Add
from keras.utils import np_utils
from keras.datasets import weather_classification

然后，读取和预处理数据集：

(x_train, y_train), (x_test, y_test) = weather_classification.load_data()

# 将像素值缩放到0-1之间
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.

# 将标签进行One-Hot编码
y_train = np_utils.to_categorical(y_train, 4)
y_test = np_utils.to_categorical(y_test, 4)

接下来，定义ResNet网络：

def Conv2d_BN(x, nb_filter, kernel_size, strides=(1,1), padding='same', name=None):
    if name is not None:
        bn_name = name + '_bn'
        conv_name = name + '_conv'
    else:
        bn_name = None
        conv_name = None

    x = Conv2D(nb_filter, kernel_size, padding=padding, strides=strides, activation='relu', name=conv_name)(x)
    x = BatchNormalization(axis=3, name=bn_name)(x)
    return x

def identity_block(input_tensor, nb_filter, kernel_size, name=None):
    if name is not None:
        conv_name_base = name + '_branch'
    else:
        conv_name_base = None

    x = Conv2d_BN(input_tensor, nb_filter=nb_filter, kernel_size=kernel_size, name=conv_name_base + '2a')
    x = Conv2d_BN(x, nb_filter=nb_filter, kernel_size=kernel_size, name=conv_name_base + '2b')
    x = Add()([x, input_tensor])
    x = Activation('relu')(x)
    return x

def conv_block(input_tensor, nb_filter, kernel_size, strides=(2, 2), name=None):
    if name is not None:
        conv_name_base = name + '_branch'
    else:
        conv_name_base = None

    x = Conv2d_BN(input_tensor, nb_filter=nb_filter, kernel_size=kernel_size, strides=strides, name=conv_name_base + '2a')
    x = Conv2d_BN(x, nb_filter=nb_filter, kernel_size=kernel_size, name=conv_name_base + '2b')
    shortcut = Conv2d_BN(input_tensor, nb_filter=nb_filter, kernel_size=1, strides=strides, name=conv_name_base + '1')
    x = Add()([x, shortcut])
    x = Activation('relu')(x)
    return x

def ResNet(input_shape, nb_classes):
    input_tensor = Input(shape=input_shape)
    x = Conv2d_BN(input_tensor, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='same', name='conv1')
    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

    x = conv_block(x, nb_filter=64, kernel_size=(3, 3), strides=(1, 1), name='conv2_1')
    x = identity_block(x, nb_filter=64, kernel_size=(3, 3), name='conv2_2')
    x = identity_block(x, nb_filter=64, kernel_size=(3, 3), name='conv2_3')

    x = conv_block(x, nb_filter=128, kernel_size=(3, 3), name='conv3_1')
    x = identity_block(x, nb_filter=128, kernel_size=(3, 3), name='conv3_2')
    x = identity_block(x, nb_filter=128, kernel_size=(3, 3), name='conv3_3')
    x = identity_block(x, nb_filter=128, kernel_size=(3, 3), name='conv3_4')

    x = conv_block(x, nb_filter=256, kernel_size=(3, 3), name='conv4_1')
    x = identity_block(x, nb_filter=256, kernel_size=(3, 3), name='conv4_2')
    x = identity_block(x, nb_filter=256, kernel_size=(3, 3), name='conv4_3')
    x = identity_block(x, nb_filter=256, kernel_size=(3, 3), name='conv4_4')
    x = identity_block(x, nb_filter=256, kernel_size=(3, 3), name='conv4_5')
    x = identity_block(x, nb_filter=256, kernel_size=(3, 3), name='conv4_6')

    x = conv_block(x, nb_filter=512, kernel_size=(3, 3), name='conv5_1')
    x = identity_block(x, nb_filter=512, kernel_size=(3, 3), name='conv5_2')
    x = identity_block(x, nb_filter=512, kernel_size=(3, 3), name='conv5_3')
    x = Flatten()(x)
    x = Dense(nb_classes, activation='softmax', name='fc')(x)

    model = Model(input_tensor, x)
    return model

然后，编译和训练网络：

model = ResNet(input_shape=(64, 64, 3), nb_classes=4)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()

history = model.fit(x_train, y_train, batch_size=64, epochs=20, validation_data=(x_test, y_test))

最后，评估和测试网络，并绘制图像：

# 评估网络
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# 绘制训练和测试曲线
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()

# 绘制混淆矩阵
from sklearn.metrics import confusion_matrix
import itertools

def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print('Normalized confusion matrix')
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

y_pred = model.predict(x_test)
y_pred_classes = np.argmax(y_pred, axis=1)
y_true = np.argmax(y_test, axis=1)
confusion_mtx = confusion_matrix(y_true, y_pred_classes)
plot_confusion_matrix(confusion_mtx, classes=['sunny', 'cloudy', 'rainy', 'snowy'])

希望以上代码能够帮助您完成ResNet网络对四种天气的分类任务，并绘制相应的图像。