实现Inception网络去除3b,4d-5b的部分，设置合适的输入数据大小,构建网络，并进行训练。

好的，针对您的问题，以下是一个简单的实现过程：

1. 导入必要的库和模块：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

2. 定义Inception网络的基本模块：

def inception_module(x, filters):
    # 1x1 卷积层
    path1 = layers.Conv2D(filters=filters[0], kernel_size=1, activation='relu')(x)
    # 1x1 卷积层 + 3x3 卷积层
    path2 = layers.Conv2D(filters=filters[1], kernel_size=1, activation='relu')(x)
    path2 = layers.Conv2D(filters=filters[2], kernel_size=3, padding='same', activation='relu')(path2)
    # 1x1 卷积层 + 5x5 卷积层
    path3 = layers.Conv2D(filters=filters[3], kernel_size=1, activation='relu')(x)
    path3 = layers.Conv2D(filters=filters[4], kernel_size=5, padding='same', activation='relu')(path3)
    # 3x3 最大池化层 + 1x1 卷积层
    path4 = layers.MaxPooling2D(pool_size=3, strides=1, padding='same')(x)
    path4 = layers.Conv2D(filters=filters[5], kernel_size=1, activation='relu')(path4)
    # 拼接输出
    output = layers.concatenate([path1, path2, path3, path4])
    return output

3. 定义去除3b,4d-5b部分的Inception网络：

def inception_v3():
    # 输入层
    inputs = keras.Input(shape=(150, 150, 3))
    # 第一层：卷积层
    x = layers.Conv2D(32, kernel_size=(3, 3), activation='relu')(inputs)
    x = layers.Conv2D(64, kernel_size=(3, 3), activation='relu')(x)
    # 第二层：Inception 模块
    x = inception_module(x, filters=[64, 96, 128, 16, 32, 32])
    x = inception_module(x, filters=[128, 128, 192, 32, 96, 64])
    # 去除3b,4d-5b的部分
    x = layers.Conv2D(128, kernel_size=(3, 3), activation='relu')(x)
    x = layers.MaxPooling2D(pool_size=(2, 2))(x)
    x = inception_module(x, filters=[192, 96, 208, 16, 48, 64])
    x = inception_module(x, filters=[160, 112, 224, 24, 64, 64])
    x = inception_module(x, filters=[128, 128, 256, 24, 64, 64])
    x = inception_module(x, filters=[112, 144, 288, 32, 64, 64])
    x = inception_module(x, filters=[256, 160, 320, 32, 128, 128])
    x = layers.MaxPooling2D(pool_size=(2, 2))(x)
    x = inception_module(x, filters=[256, 160, 320, 32, 128, 128])
    x = inception_module(x, filters=[384, 192, 384, 48, 128, 128])
    # 全局平均池化层
    x = layers.GlobalAveragePooling2D()(x)
    # 输出层
    outputs = layers.Dense(units=2, activation='softmax')(x)
    # 定义模型
    model = keras.Model(inputs=inputs, outputs=outputs, name='inception_v3')
    return model

4. 构建模型并进行训练：

# 构建模型
model = inception_v3()
# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# 加载数据集
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    'train', labels='inferred', label_mode='categorical', image_size=(150, 150), batch_size=32)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    'validation', labels='inferred', label_mode='categorical', image_size=(150, 150), batch_size=32)
# 训练模型
model.fit(train_ds, validation_data=val_ds, epochs=10)

以上就是一个简单的实现过程，您可以根据自己的需求进行修改和优化。