问将单个图像输入到H5文件中，并获取每个类的百分比作为输出

EN

import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')

tf.random.set_seed(1234)

train_data_dir="X_train_data/"

#Used Sequential
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(8, (3, 3), activation='relu', input_shape=(64, 64, 3)))
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2), strides=None, padding="valid"))
model.add(tf.keras.layers.Conv2D(16, (3, 3), activation='relu'))
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2), strides=None, padding="valid"))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(32))
model.add(tf.keras.layers.Dropout(.1, input_shape=(32,)))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

#Defining optimizer
from tensorflow.keras.optimizers import Adam

model.compile(optimizer=Adam(lr=0.0001), loss='binary_crossentropy', metrics=['accuracy'])

# Directing Images to train folder
from tensorflow.keras.preprocessing.image import ImageDataGenerator
img_height, img_width = 64,64
batch_size = 16
train_datagen = ImageDataGenerator(validation_split=0.3) # set validation split

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='binary',
    subset='training') # set as training data

# Splitting images for validation set
validation_generator = train_datagen.flow_from_directory(
    train_data_dir, # same directory as training data
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='binary',
    subset='validation') # set as validation data

# Training the model
trainer=model.fit(train_generator, validation_data=validation_generator, epochs=10, verbose=2)

model.save("cnn_covid_x-ray_v1.h5") #you can load this model from output part