From fdc2558da9d70dd6e4133d9cbb234c4423b84e90 Mon Sep 17 00:00:00 2001
From: root
 <root@inss-62a9d72c3b452e2a31780ace-iprj68564c64e46d2454ac0aba12.inss-62a9d72c3b452e2a31780ace-iprj68564c64e46d2454ac0aba12.inscode.svc.cluster.local>
Date: Wed, 23 Jul 2025 12:57:00 +0800
Subject: [PATCH] Wed Jul 23 12:57:00 CST 2025 inscode

---
 test01.py | 40 ++++++++++++++++++++++++++++++++++++++++
 test02.py | 31 +++++++++++++++++++++++++++++++
 2 files changed, 71 insertions(+)
 create mode 100644 test01.py
 create mode 100644 test02.py

diff --git a/test01.py b/test01.py
new file mode 100644
index 0000000..54be4af
--- /dev/null
+++ b/test01.py
@@ -0,0 +1,40 @@
+import numpy as np
+
+class SimpleRNN:
+    def __init__(self, input_size, hidden_size):
+        self.Wx = np.random.randn(hidden_size, input_size)  # 输入权重
+        self.Wh = np.random.randn(hidden_size, hidden_size)  # 隐藏状态权重
+        self.b = np.zeros((hidden_size, 1))  # 偏置项
+    
+    def forward(self, x, h_prev):
+        h_next = np.tanh(np.dot(self.Wx, x) + np.dot(self.Wh, h_prev) + self.b)
+        return h_next
+
+# 测试 SimpleRNN
+def test_simple_rnn():
+    # 参数设置
+    input_size = 30   # 输入向量的维度
+    hidden_size = 20  # 隐藏状态的维度
+    seq_length = 5   # 序列长度
+
+    # 初始化 RNN
+    rnn = SimpleRNN(input_size, hidden_size)
+
+    # 生成随机输入序列 (input_size, seq_length)
+    np.random.seed(42)  # 固定随机种子以便复现结果
+    x_sequence = [np.random.randn(input_size, 1) for _ in range(seq_length)]  # 每个时间步的输入 (input_size, 1)
+
+    # 初始隐藏状态 (hidden_size, 1)
+    h = np.zeros((hidden_size, 1))
+
+    # 前向传播
+    print("RNN 前向传播测试:")
+    for t, x in enumerate(x_sequence):
+        h = rnn.forward(x, h)
+        print(f"时间步 {t}:")
+        print(f"输入 x_{t} 的形状: {x.shape}, 值:\n{x.T}")
+        print(f"隐藏状态 h_{t} 的形状: {h.shape}, 值:\n{h.T}")
+        print("-" * 50)
+
+if __name__ == "__main__":
+    test_simple_rnn()
\ No newline at end of file
diff --git a/test02.py b/test02.py
new file mode 100644
index 0000000..93bbc26
--- /dev/null
+++ b/test02.py
@@ -0,0 +1,31 @@
+import torch
+
+# 创建一个 2x3 的全 0 张量
+a = torch.zeros(2, 3)
+print(a)
+
+# 创建一个 2x3 的全 1 张量
+b = torch.ones(2, 3)
+print(b)
+
+# 创建一个 2x3 的随机数张量
+c = torch.randn(2, 3)
+print(c)
+
+# 从 NumPy 数组创建张量
+import numpy as np
+numpy_array = np.array([[1, 2], [3, 4]])
+tensor_from_numpy = torch.from_numpy(numpy_array)
+print(tensor_from_numpy)
+
+# 在指定设备（CPU/GPU）上创建张量
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+d = torch.randn(2, 3, device=device)
+print(torch.cuda.is_available())
+print(d)
+
+# 逐元素乘法（要求形状相同）
+X = torch.tensor([[1, 2], [3, 4]])
+Y = torch.tensor([[5, 6], [7, 8]])
+Z = X * Y  # 或 torch.mul(X, Y)
+print("逐元素乘法结果:\n", Z)
\ No newline at end of file
-- 
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