鸢尾花数据集分类问题（1）https://developer.aliyun.com/article/1540968
2.数据集乱序
tf.random.set_seed(116) # 设计随机种子，以生成同样的标签序列，使x_data和y_data一一对应 x_data = tf.random.shuffle(x_data,116) # 数据集乱序 y_data = tf.random.shuffle(y_data,116) print(x_data) print(y_data)
tf.Tensor( [[5.6 3. 4.5 1.5] [4.9 2.4 3.3 1. ] [6.7 3. 5.2 2.3] [4.6 3.2 1.4 0.2] [5.1 3.5 1.4 0.2] [5.4 3.7 1.5 0.2] [6.8 3.2 5.9 2.3] [5.3 3.7 1.5 0.2] [4.6 3.4 1.4 0.3] [4.8 3. 1.4 0.1] [6.5 3. 5.2 2. ] [4.9 2.5 4.5 1.7] [6.8 3. 5.5 2.1] [4.8 3. 1.4 0.3] [5.7 2.5 5. 2. ] [7.9 3.8 6.4 2. ] [5. 3.4 1.6 0.4] [5.9 3.2 4.8 1.8] [6. 2.2 5. 1.5] [7.7 2.8 6.7 2. ] [5.5 2.3 4. 1.3] [4.9 3. 1.4 0.2] [5.8 2.8 5.1 2.4] [5.4 3.4 1.5 0.4] [6.4 2.8 5.6 2.2] [5.8 2.6 4. 1.2] [5.6 2.7 4.2 1.3] [4.9 3.1 1.5 0.2] [6.7 3.1 4.7 1.5] [5.5 4.2 1.4 0.2] [7.2 3. 5.8 1.6] [6.3 2.9 5.6 1.8] [6.3 2.5 5. 1.9] [6.9 3.1 5.1 2.3] [6.4 3.2 5.3 2.3] [6.7 3.1 4.4 1.4] [5. 3.2 1.2 0.2] [6.3 3.3 6. 2.5] [6. 2.2 4. 1. ] [6.3 2.5 4.9 1.5] [6.5 3. 5.8 2.2] [7.2 3.6 6.1 2.5] [6.7 3. 5. 1.7] [6. 3. 4.8 1.8] [5.7 2.8 4.1 1.3] [5.7 4.4 1.5 0.4] [5.4 3.9 1.7 0.4] [5.6 2.5 3.9 1.1] [7.2 3.2 6. 1.8] [4.7 3.2 1.6 0.2] [5.1 3.4 1.5 0.2] [5. 3.6 1.4 0.2] [5.5 2.4 3.8 1.1] [5.7 2.8 4.5 1.3] [5.2 3.4 1.4 0.2] [7.6 3. 6.6 2.1] [6.3 3.3 4.7 1.6] [5. 2.3 3.3 1. ] [5.5 2.6 4.4 1.2] [4.8 3.4 1.9 0.2] [5.5 3.5 1.3 0.2] [5.1 3.5 1.4 0.3] [5.8 2.7 5.1 1.9] [4.6 3.6 1. 0.2] [5.2 2.7 3.9 1.4] [5.7 3. 4.2 1.2] [6.3 2.7 4.9 1.8] [6. 2.9 4.5 1.5] [6.7 3.3 5.7 2.5] [6.7 3.3 5.7 2.1] [4.6 3.1 1.5 0.2] [6.9 3.1 5.4 2.1] [6.2 2.8 4.8 1.8] [5.5 2.5 4. 1.3] [6.1 2.9 4.7 1.4] [5.7 3.8 1.7 0.3] [5.6 2.8 4.9 2. ] [6.6 3. 4.4 1.4] [4.7 3.2 1.3 0.2] [6.6 2.9 4.6 1.3] [6.1 3. 4.9 1.8] [6.3 2.8 5.1 1.5] [6.5 2.8 4.6 1.5] [5.1 3.7 1.5 0.4] [7.4 2.8 6.1 1.9] [4.9 3.1 1.5 0.1] [4.8 3.1 1.6 0.2] [5.5 2.4 3.7 1. ] [5.2 4.1 1.5 0.1] [5.4 3. 4.5 1.5] [6.7 3.1 5.6 2.4] [5.1 3.8 1.5 0.3] [4.4 3.2 1.3 0.2] [7.1 3. 5.9 2.1] [5. 3.3 1.4 0.2] [5. 3.5 1.6 0.6] [6.4 3.2 4.5 1.5] [6.1 3. 4.6 1.4] [6.3 2.3 4.4 1.3] [6.5 3. 5.5 1.8] [7.7 3.8 6.7 2.2] [6.3 3.4 5.6 2.4] [6.4 2.9 4.3 1.3] [5. 3. 1.6 0.2] [5.8 2.7 4.1 1. ] [5.8 2.7 3.9 1.2] [4.8 3.4 1.6 0.2] [6.9 3.1 4.9 1.5] [5.8 4. 1.2 0.2] [6. 3.4 4.5 1.6] [5.4 3.9 1.3 0.4] [6.1 2.8 4. 1.3] [5.4 3.4 1.7 0.2] [7.7 3. 6.1 2.3] [5.1 2.5 3. 1.1] [5.6 3. 4.1 1.3] [6.1 2.8 4.7 1.2] [6.2 3.4 5.4 2.3] [6.4 2.8 5.6 2.1] [5.7 2.6 3.5 1. ] [5.1 3.8 1.9 0.4] [5.2 3.5 1.5 0.2] [6.9 3.2 5.7 2.3] [4.5 2.3 1.3 0.3] [4.4 3. 1.3 0.2] [6.5 3.2 5.1 2. ] [4.3 3. 1.1 0.1] [5.7 2.9 4.2 1.3] [5.8 2.7 5.1 1.9] [6.4 3.1 5.5 1.8] [6.2 2.9 4.3 1.3] [5.6 2.9 3.6 1.3] [4.9 3.6 1.4 0.1] [6.2 2.2 4.5 1.5] [4.4 2.9 1.4 0.2] [5. 3.5 1.3 0.3] [5.9 3. 5.1 1.8] [6.1 2.6 5.6 1.4] [5.1 3.8 1.6 0.2] [6.8 2.8 4.8 1.4] [5.9 3. 4.2 1.5] [6.7 2.5 5.8 1.8] [7. 3.2 4.7 1.4] [7.7 2.6 6.9 2.3] [6. 2.7 5.1 1.6] [5.1 3.3 1.7 0.5] [5. 2. 3.5 1. ] [6.4 2.7 5.3 1.9] [5. 3.4 1.5 0.2] [7.3 2.9 6.3 1.8]], shape=(150, 4), dtype=float64) tf.Tensor( [1 1 2 0 0 0 2 0 0 0 2 2 2 0 2 2 0 1 2 2 1 0 2 0 2 1 1 0 1 0 2 2 2 2 2 1 0 2 1 1 2 2 1 2 1 0 0 1 2 0 0 0 1 1 0 2 1 1 1 0 0 0 2 0 1 1 2 1 2 2 0 2 2 1 1 0 2 1 0 1 2 2 1 0 2 0 0 1 0 1 2 0 0 2 0 0 1 1 1 2 2 2 1 0 1 1 0 1 0 1 0 1 0 2 1 1 1 2 2 1 0 0 2 0 0 2 0 1 2 2 1 1 0 1 0 0 2 2 0 1 1 2 1 2 1 0 1 2 0 2], shape=(150,), dtype=int32)
3.划分训练集/测试集
x_train = x_data[:-30] y_train = y_data[:-30] x_test = x_data[-30:] y_test = y_data[-30:] print(y_train)
tf.Tensor( [1 1 2 0 0 0 2 0 0 0 2 2 2 0 2 2 0 1 2 2 1 0 2 0 2 1 1 0 1 0 2 2 2 2 2 1 0 2 1 1 2 2 1 2 1 0 0 1 2 0 0 0 1 1 0 2 1 1 1 0 0 0 2 0 1 1 2 1 2 2 0 2 2 1 1 0 2 1 0 1 2 2 1 0 2 0 0 1 0 1 2 0 0 2 0 0 1 1 1 2 2 2 1 0 1 1 0 1 0 1 0 1 0 2 1 1 1 2 2 1], shape=(120,), dtype=int32)
4.配对成[特征值,标签]对
划分batch，之后每次喂入一个batch训练
train_db = tf.data.Dataset.from_tensor_slices((x_train,y_train)).batch(32) # 打包成30个batch test_db = tf.data.Dataset.from_tensor_slices((x_test,y_test)).batch(32) print(train_db) print(test_db)
<BatchDataset element_spec=(TensorSpec(shape=(None, 4), dtype=tf.float64, name=None), TensorSpec(shape=(None,), dtype=tf.int32, name=None))> <BatchDataset element_spec=(TensorSpec(shape=(None, 4), dtype=tf.float64, name=None), TensorSpec(shape=(None,), dtype=tf.int32, name=None))>
list(train_db.as_numpy_iterator())
[(array([[5.6, 3. , 4.5, 1.5], [4.9, 2.4, 3.3, 1. ], [6.7, 3. , 5.2, 2.3], [4.6, 3.2, 1.4, 0.2], [5.1, 3.5, 1.4, 0.2], [5.4, 3.7, 1.5, 0.2], [6.8, 3.2, 5.9, 2.3], [5.3, 3.7, 1.5, 0.2], [4.6, 3.4, 1.4, 0.3], [4.8, 3. , 1.4, 0.1], [6.5, 3. , 5.2, 2. ], [4.9, 2.5, 4.5, 1.7], [6.8, 3. , 5.5, 2.1], [4.8, 3. , 1.4, 0.3], [5.7, 2.5, 5. , 2. ], [7.9, 3.8, 6.4, 2. ], [5. , 3.4, 1.6, 0.4], [5.9, 3.2, 4.8, 1.8], [6. , 2.2, 5. , 1.5], [7.7, 2.8, 6.7, 2. ], [5.5, 2.3, 4. , 1.3], [4.9, 3. , 1.4, 0.2], [5.8, 2.8, 5.1, 2.4], [5.4, 3.4, 1.5, 0.4], [6.4, 2.8, 5.6, 2.2], [5.8, 2.6, 4. , 1.2], [5.6, 2.7, 4.2, 1.3], [4.9, 3.1, 1.5, 0.2], [6.7, 3.1, 4.7, 1.5], [5.5, 4.2, 1.4, 0.2], [7.2, 3. , 5.8, 1.6], [6.3, 2.9, 5.6, 1.8]]), array([1, 1, 2, 0, 0, 0, 2, 0, 0, 0, 2, 2, 2, 0, 2, 2, 0, 1, 2, 2, 1, 0, 2, 0, 2, 1, 1, 0, 1, 0, 2, 2])), (array([[6.3, 2.5, 5. , 1.9], [6.9, 3.1, 5.1, 2.3], [6.4, 3.2, 5.3, 2.3], [6.7, 3.1, 4.4, 1.4], [5. , 3.2, 1.2, 0.2], [6.3, 3.3, 6. , 2.5], [6. , 2.2, 4. , 1. ], [6.3, 2.5, 4.9, 1.5], [6.5, 3. , 5.8, 2.2], [7.2, 3.6, 6.1, 2.5], [6.7, 3. , 5. , 1.7], [6. , 3. , 4.8, 1.8], [5.7, 2.8, 4.1, 1.3], [5.7, 4.4, 1.5, 0.4], [5.4, 3.9, 1.7, 0.4], [5.6, 2.5, 3.9, 1.1], [7.2, 3.2, 6. , 1.8], [4.7, 3.2, 1.6, 0.2], [5.1, 3.4, 1.5, 0.2], [5. , 3.6, 1.4, 0.2], [5.5, 2.4, 3.8, 1.1], [5.7, 2.8, 4.5, 1.3], [5.2, 3.4, 1.4, 0.2], [7.6, 3. , 6.6, 2.1], [6.3, 3.3, 4.7, 1.6], [5. , 2.3, 3.3, 1. ], [5.5, 2.6, 4.4, 1.2], [4.8, 3.4, 1.9, 0.2], [5.5, 3.5, 1.3, 0.2], [5.1, 3.5, 1.4, 0.3], [5.8, 2.7, 5.1, 1.9], [4.6, 3.6, 1. , 0.2]]), array([2, 2, 2, 1, 0, 2, 1, 1, 2, 2, 1, 2, 1, 0, 0, 1, 2, 0, 0, 0, 1, 1, 0, 2, 1, 1, 1, 0, 0, 0, 2, 0])), (array([[5.2, 2.7, 3.9, 1.4], [5.7, 3. , 4.2, 1.2], [6.3, 2.7, 4.9, 1.8], [6. , 2.9, 4.5, 1.5], [6.7, 3.3, 5.7, 2.5], [6.7, 3.3, 5.7, 2.1], [4.6, 3.1, 1.5, 0.2], [6.9, 3.1, 5.4, 2.1], [6.2, 2.8, 4.8, 1.8], [5.5, 2.5, 4. , 1.3], [6.1, 2.9, 4.7, 1.4], [5.7, 3.8, 1.7, 0.3], [5.6, 2.8, 4.9, 2. ], [6.6, 3. , 4.4, 1.4], [4.7, 3.2, 1.3, 0.2], [6.6, 2.9, 4.6, 1.3], [6.1, 3. , 4.9, 1.8], [6.3, 2.8, 5.1, 1.5], [6.5, 2.8, 4.6, 1.5], [5.1, 3.7, 1.5, 0.4], [7.4, 2.8, 6.1, 1.9], [4.9, 3.1, 1.5, 0.1], [4.8, 3.1, 1.6, 0.2], [5.5, 2.4, 3.7, 1. ], [5.2, 4.1, 1.5, 0.1], [5.4, 3. , 4.5, 1.5], [6.7, 3.1, 5.6, 2.4], [5.1, 3.8, 1.5, 0.3], [4.4, 3.2, 1.3, 0.2], [7.1, 3. , 5.9, 2.1], [5. , 3.3, 1.4, 0.2], [5. , 3.5, 1.6, 0.6]]), array([1, 1, 2, 1, 2, 2, 0, 2, 2, 1, 1, 0, 2, 1, 0, 1, 2, 2, 1, 0, 2, 0, 0, 1, 0, 1, 2, 0, 0, 2, 0, 0])), (array([[6.4, 3.2, 4.5, 1.5], [6.1, 3. , 4.6, 1.4], [6.3, 2.3, 4.4, 1.3], [6.5, 3. , 5.5, 1.8], [7.7, 3.8, 6.7, 2.2], [6.3, 3.4, 5.6, 2.4], [6.4, 2.9, 4.3, 1.3], [5. , 3. , 1.6, 0.2], [5.8, 2.7, 4.1, 1. ], [5.8, 2.7, 3.9, 1.2], [4.8, 3.4, 1.6, 0.2], [6.9, 3.1, 4.9, 1.5], [5.8, 4. , 1.2, 0.2], [6. , 3.4, 4.5, 1.6], [5.4, 3.9, 1.3, 0.4], [6.1, 2.8, 4. , 1.3], [5.4, 3.4, 1.7, 0.2], [7.7, 3. , 6.1, 2.3], [5.1, 2.5, 3. , 1.1], [5.6, 3. , 4.1, 1.3], [6.1, 2.8, 4.7, 1.2], [6.2, 3.4, 5.4, 2.3], [6.4, 2.8, 5.6, 2.1], [5.7, 2.6, 3.5, 1. ]]), array([1, 1, 1, 2, 2, 2, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 2, 1, 1, 1, 2, 2, 1]))]
5.定义神经网络中所有可训练的参数
# 搭一个1层的神经网络 w1 = tf.Variable(tf.random.truncated_normal([4,3],stddev=0.1,seed=1)) b1 = tf.Variable(tf.random.truncated_normal([3],stddev=0.1,seed=1)) # w2 = tf.Variable(tf.random.truncated_normal([5,3],stddev=0.1,seed=1)) # b2 = tf.Variable(tf.random.truncated_normal([3],stddev=0.1,seed=1))
鸢尾花数据集分类问题（3）https://developer.aliyun.com/article/1540970
