Add files via upload

      while True:

        end = min((start + 1) * batch_size, X.shape[0])

        X_batch = X[(start * batch_size):end]

        output = self.sess.run([self.outputs], feed_dict={self.inputs:

                                                          X_batch})[0]

        output = self.sess.run(

            [self.outputs], feed_dict={self.inputs: X_batch})[0]

        X_out.append(output)

        num_transformed = num_transformed + X_batch.shape[0]

        print('%i samples transformed' % num_transformed)

      radial_sym = self.radial_symmetry(d_radial_cutoff, d, atom_numbers)

      angular_sym = self.angular_symmetry(d_angular_cutoff, d, atom_numbers,

                                          coordinates)

      self.outputs = tf.concat([

          tf.to_float(tf.expand_dims(atom_numbers, 2)), radial_sym, angular_sym

      self.outputs = tf.concat(

          [

              tf.to_float(tf.expand_dims(atom_numbers, 2)), radial_sym,

              angular_sym

          ],

          axis=2)

    return graph

          zoom_x - amount of zoom along x axis

          zoom_y - amount of zoom along y axis

          mode - Points outside the boundaries of the input are filled according to the given mode

          (‘constant’, ‘nearest’, ‘reflect’ or ‘wrap’). Default is ‘constant’

          (‘constant’, ‘nearest’, ‘reflect’ or ‘wrap’). Default is ‘constant’.

          order - The order of the spline interpolation, default is 3. The order has to be in the range 0-5.

          """

    h, w = self.Image.shape[0], self.Image.shape[1]

    return x

  def random_noise(self, mode='gaussian', seed=None):

    '''

      mode: the type of noise to add (gaussian,localvar,poisson,salt,pepper,s&p,speckle

      seed: random seed

    '''Adds random noise to the image

    Parameters:

      mode - the type of noise to add (gaussian,localvar,poisson,salt,pepper,s&p,speckle

      seed - seed for the random generator.

        '''

    x = (self.Image - np.min(self.Image)) / (