解决issue7

猫狗识别/model.py

@@ -1,5 +1,5 @@
-#coding=utf-8  
-import tensorflow as tf  
+import tensorflow as tf
+from tensorflow.keras import layers, models
 # 结构
 # conv1   卷积层 1
 # pooling1_lrn  池化层 1
@@ -8,104 +8,59 @@
 # local3 全连接层 1
 # local4 全连接层 2
 # softmax 全连接层 3
-def inference(images, batch_size, n_classes):  
-
-    with tf.variable_scope('conv1') as scope: 
-     # 卷积盒的为 3*3 的卷积盒，图片厚度是3，输出是16个featuremap
-        weights = tf.get_variable('weights',  
-                                  shape=[3, 3, 3, 16],  
-                                  dtype=tf.float32,  
-                                  initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))  
-        biases = tf.get_variable('biases',  
-                                 shape=[16],  
-                                 dtype=tf.float32,  
-                                 initializer=tf.constant_initializer(0.1))  
-        conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding='SAME')  
-        pre_activation = tf.nn.bias_add(conv, biases)  
-        conv1 = tf.nn.relu(pre_activation, name=scope.name)  
-
-    with tf.variable_scope('pooling1_lrn') as scope:  
-            pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pooling1')  
-            norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1')  
-
-    with tf.variable_scope('conv2') as scope:  
-                weights = tf.get_variable('weights',  
-                                          shape=[3, 3, 16, 16],  
-                                          dtype=tf.float32,  
-                                          initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))  
-                biases = tf.get_variable('biases',  
-                                         shape=[16],  
-                                         dtype=tf.float32,  
-                                         initializer=tf.constant_initializer(0.1))  
-                conv = tf.nn.conv2d(norm1, weights, strides=[1, 1, 1, 1], padding='SAME')  
-                pre_activation = tf.nn.bias_add(conv, biases)  
-                conv2 = tf.nn.relu(pre_activation, name='conv2')  
-
-    # pool2 and norm2  
-    with tf.variable_scope('pooling2_lrn') as scope:  
-        norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2')  
-        pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1], padding='SAME', name='pooling2')  
-
-    with tf.variable_scope('local3') as scope:  
-        reshape = tf.reshape(pool2, shape=[batch_size, -1])  
-        dim = reshape.get_shape()[1].value  
-        weights = tf.get_variable('weights',  
-                                  shape=[dim, 128],  
-                                  dtype=tf.float32,  
-                                  initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))  
-        biases = tf.get_variable('biases',  
-                                 shape=[128],  
-                                 dtype=tf.float32,  
-                                 initializer=tf.constant_initializer(0.1))  
-    local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)  
-
-    # local4  
-    with tf.variable_scope('local4') as scope:  
-        weights = tf.get_variable('weights',  
-                                  shape=[128, 128],  
-                                  dtype=tf.float32,  
-                                  initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))  
-        biases = tf.get_variable('biases',  
-                                 shape=[128],  
-                                 dtype=tf.float32,  
-                                 initializer=tf.constant_initializer(0.1))  
-        local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4')  
-
-    # softmax  
-    with tf.variable_scope('softmax_linear') as scope:  
-        weights = tf.get_variable('softmax_linear',  
-                                  shape=[128, n_classes],  
-                                  dtype=tf.float32,  
-                                  initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))  
-        biases = tf.get_variable('biases',  
-                                 shape=[n_classes],  
-                                 dtype=tf.float32,  
-                                 initializer=tf.constant_initializer(0.1))  
-        softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear')  
-
-    return softmax_linear  
-
-
-
-def losses(logits, labels):  
-    with tf.variable_scope('loss') as scope:  
-        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits \
-                        (logits=logits, labels=labels, name='xentropy_per_example')  
-        loss = tf.reduce_mean(cross_entropy, name='loss')  
-        tf.summary.scalar(scope.name + '/loss', loss)  
-    return loss  
-
-def trainning(loss, learning_rate):  
-    with tf.name_scope('optimizer'):  
-        optimizer = tf.train.AdamOptimizer(learning_rate= learning_rate)  
-        global_step = tf.Variable(0, name='global_step', trainable=False)  
-        train_op = optimizer.minimize(loss, global_step= global_step)  
-    return train_op  
-
-def evaluation(logits, labels):  
-    with tf.variable_scope('accuracy') as scope:  
-        correct = tf.nn.in_top_k(logits, labels, 1)  
-        correct = tf.cast(correct, tf.float16)  
-        accuracy = tf.reduce_mean(correct)  
-        tf.summary.scalar(scope.name + '/accuracy', accuracy)  
-    return accuracy
+
+def inference(input_shape, n_classes):
+    model = models.Sequential()
+
+    #修改
+    model.add(layers.Input(shape=input_shape))  # 使用 Input 层定义输入形状
+
+    # Conv1，第一个卷积层，使用3x3的卷积核，输出16个特征图，使用ReLU激活函数
+    model.add(layers.Conv2D(16, (3, 3), activation='relu', padding='same', name='conv1'))
+
+    # Pooling1_lrn，添加一个最大池化层，使用3x3的池化窗口，步幅为2x2，然后进行批量归一化
+    model.add(layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='pooling1'))
+    model.add(layers.BatchNormalization(name='norm1'))
+
+    # Conv2，第二个卷积层，使用3x3的卷积核，输出16个特征图，使用ReLU激活函数
+    model.add(layers.Conv2D(16, (3, 3), activation='relu', padding='same', name='conv2'))
+
+    # Pooling2_lrn，进行批量归一化，然后添加一个最大池化层，使用3x3的池化窗口，步幅为1x1
+    model.add(layers.BatchNormalization(name='norm2'))
+    model.add(layers.MaxPooling2D((3, 3), strides=(1, 1), padding='same', name='pooling2'))
+
+    # Flatten，将多维输入一维化，为全连接层做准备
+    model.add(layers.Flatten())
+
+    # Local3，第一个全连接层，有128个神经元，使用ReLU激活函数
+    model.add(layers.Dense(128, activation='relu', name='local3'))
+
+    # Local4，第二个全连接层，有128个神经元，使用ReLU激活函数
+    model.add(layers.Dense(128, activation='relu', name='local4'))
+
+    # Softmax，输出层，有n_classes个神经元，使用softmax激活函数
+    model.add(layers.Dense(n_classes, activation='softmax', name='softmax_linear'))
+
+    return model
+
+
+# 计算模型的损失，SparseCategoricalCrossentropy是一个用于多分类问题的损失函数，适用于标签是整数的情况
+def losses(logits, labels):
+    loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+    loss = loss_fn(labels, logits)
+    return loss
+
+
+# 定义模型的训练过程
+def trainning(model, loss, learning_rate):
+    # 使用Adam优化器，learning_rate是学习率
+    optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
+    # compile: 编译模型，指定优化器、损失函数和评估指标（准确率）
+    model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])
+    return model
+
+
+# 评估模型的性能
+def evaluation(model, images, labels):
+    loss, accuracy = model.evaluate(images, labels)
+    return accuracy