title: File ~\anaconda3\lib\site-packages\numpy\lib\_datasource.py:533 in open raise IOError("%s not found." % path)
body: <p>I have this code:</p>
  <pre><code>import numpy as np
  
  
  #from bp.deep_bp import deep_train
  def deep_train(train_x, train_y, test_x, test_y, layers_dims):
      parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations=2500, print_cost=True)
      pred_train = predict(train_x, train_y, parameters)
      pred_test = predict(test_x, test_x, parameters)
      
  #from bp.shallow_bp import shallow_train
  def shallow_train(train_set_x, train_set_y, test_set_x, test_set_y, sys_layers_dims):
       dataset = load_resize_dataset()
       train_set_x = dataset['train_set_x']
       train_set_y = dataset['train_set_y']
       test_set_x = dataset['test_set_x']
       test_set_y = dataset['test_set_y']
       d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations=2000, learning_rate=0.0001,
          print_cost=True)
      
      
  #from factory.bp_data_factory import load_cat_dataset
  def load_cat_dataset():
              train_dataset = h5py.File('D:\research\SoftWareReliability-master\SoftWareReliability-master\resource\train_catvnoncat.h5', &quot;r&quot;)
              train_set_x_orig = np.array(train_dataset[&quot;train_set_x&quot;][:])  # train set features
              train_set_y_orig = np.array(train_dataset[&quot;train_set_y&quot;][:])  # train set labels
      
              test_dataset = h5py.File('D:\research\SoftWareReliability-master\SoftWareReliability-master\resource\test_catvnoncat.h5', &quot;r&quot;)
              test_set_x_orig = np.array(test_dataset[&quot;test_set_x&quot;][:])  # test set features
              test_set_y_orig = np.array(test_dataset[&quot;test_set_y&quot;][:])  # test set labels
      
              classes = np.array(test_dataset[&quot;list_classes&quot;][:])  # the list of classes
      
              train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
              test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
      
              return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes
          
  #from factory.graph_data_factory import load_sys_dataset     
  def load_sys_dataset():
      path = 'D:\research\SoftWareReliability-master\SoftWareReliability-master\resource\sourceData.txt'
      data = np.loadtxt(path)
      
      n = data.shape[0]
      
      data2 = np.zeros(shape=n + 1)
      data2[0] = 0
      for i in range(n):
          data2[i + 1] = data[i][1]
      
      return data2
      
      
  def shallow_bp(train_x, train_y, test_x, test_y, sys_layers_dims):
      shallow_train(train_x, train_y, test_x, test_y, sys_layers_dims)
  
  
  #def deep_dp(train_x, train_y, test_x, test_y, layers_dims):
   #   deep_train(train_x, train_y, test_x, test_y, layers_dims)
  
  
  def read_decode_cat_data():
      train_x_orig, train_y, test_x_orig, test_y, classes = load_cat_dataset()
  
      m_train = train_x_orig.shape[0]
      num_px = train_x_orig.shape[1]
      m_test = test_x_orig.shape[0]
  
      print(&quot;Number of training examples: &quot; + str(m_train))
      print(&quot;Number of testing examples: &quot; + str(m_test))
      print(&quot;Each image is of size: (&quot; + str(num_px) + &quot;, &quot; + str(num_px) + &quot;, 3)&quot;)
      print(&quot;train_x_orig shape: &quot; + str(train_x_orig.shape))
      print(&quot;train_y shape: &quot; + str(train_y.shape))
      print(&quot;test_x_orig shape: &quot; + str(test_x_orig.shape))
      print(&quot;test_y shape: &quot; + str(test_y.shape))
  
      train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0],
                                             -1).T
      test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T
  
      train_x = train_x_flatten / 255.
      test_x = test_x_flatten / 255.
  
      print(&quot;train_x's shape: &quot; + str(train_x.shape))
      print(&quot;test_x's shape: &quot; + str(test_x.shape))
  
      n_x = 12288  # num_px * num_px * 3
      n_h = 7
      n_y = 1
      layers_dims = (n_x, n_h, n_y)
      return train_x, train_y, test_x, test_y
  
  
  def Normalize(data):
      m = np.mean(data)
      mx = max(data)
      mn = min(data)
      print('mean: ' + str(m) + ' mx:' + str(mx) + ' mn:' + str(mn))
      return [(float(i) - m) / (mx - mn) for i in data]
  
  
  def read_decode_sys_data():
      y = load_sys_dataset()
      sum_count = len(y)
      y = np.array(y)
      np.delete(y, [y[0]])
      x = np.arange(start=0, stop=sum_count, step=1)
  
      x = Normalize(x)
      y = Normalize(y)
      x=np.array(x)
      y=np.array(y)
  
      last_train_index = int((sum_count - 1) * 0.8)
      train_x = x[1:last_train_index]
      train_y = y[1:last_train_index]
      test_x = x[last_train_index:]
      test_y = y[last_train_index:]
      train_x_flatten = train_x.reshape(train_x.shape[0],
                                        -1).T
      test_x_flatten = test_x.reshape(test_y.shape[0], -1).T
      train_y = train_y.reshape(1, -1)
      test_y = test_y.reshape(1, -1)
      return train_x_flatten, train_y, test_x_flatten, test_y
  
  
  if __name__ == '__main__':
      cat_layers_dims = [12288, 20, 7, 5, 1]
      sys_layers_dims = [1, 1]
      train_x, train_y, test_x, test_y = read_decode_sys_data()
      shallow_bp(train_x, train_y, test_x, test_y, sys_layers_dims)
  </code></pre>
  <p>it shows me this error:</p>
  <pre><code>Python 3.9.12 (main, Apr  4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]
  Type &quot;copyright&quot;, &quot;credits&quot; or &quot;license&quot; for more information.
  
  IPython 8.2.0 -- An enhanced Interactive Python.
  
  runfile('D:/research/SoftWareReliability-master/SoftWareReliability-master/presenter/bp_presenter.py', wdir='D:/research/SoftWareReliability-master/SoftWareReliability-master/presenter')
  Traceback (most recent call last):
  
    File D:\research\SoftWareReliability-master\SoftWareReliability-master\presenter\bp_presenter.py:133 in &lt;module&gt;
      train_x, train_y, test_x, test_y = read_decode_sys_data()
  
    File D:\research\SoftWareReliability-master\SoftWareReliability-master\presenter\bp_presenter.py:106 in read_decode_sys_data
      y = load_sys_dataset()
  
    File D:\research\SoftWareReliability-master\SoftWareReliability-master\presenter\bp_presenter.py:45 in load_sys_dataset
      data = np.loadtxt(path)
  
    File ~\anaconda3\lib\site-packages\numpy\lib\npyio.py:1067 in loadtxt
      fh = np.lib._datasource.open(fname, 'rt', encoding=encoding)
  
    File ~\anaconda3\lib\site-packages\numpy\lib\_datasource.py:193 in open
      return ds.open(path, mode, encoding=encoding, newline=newline)
  
    File ~\anaconda3\lib\site-packages\numpy\lib\_datasource.py:533 in open
      raise IOError(&quot;%s not found.&quot; % path)
  
  esource\sourceData.txt not found.r\SoftWareReliability-master
  </code></pre>
  <p>its looks in this part:</p>
  <pre><code>#from factory.graph_data_factory import load_sys_dataset     
  def load_sys_dataset():
      path = 'D:\research\SoftWareReliability-master\SoftWareReliability-master\resource\sourceData.txt'
      data = np.loadtxt(path)
      
      n = data.shape[0]
      
      data2 = np.zeros(shape=n + 1)
      data2[0] = 0
      for i in range(n):
          data2[i + 1] = data[i][1]
      
      return data2
      
  </code></pre>
  <p>but this path is 100% correct, whats the problem?</p>
  <pre><code>'D:\research\SoftWareReliability-master\SoftWareReliability-master\resource\sourceData.txt'
  </code></pre>