Row 2209

Row ID: 2209 | Dataset Entry | Axioma AXP Content Repository

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I am trying to re write some code in tensorflow, which was originally written in pytorch, but have attempted everything, including writting my own code based on theory rather than just changing the functions from one framework to another. I have also attempted using chatgpt and it didnt give me proper results. I have written some code now but I keep getting the error mentioned above (will write the full error message in the comments). Here is both the working pytorch code and the failing tensorflow code. Is there any idea of what I could be doing wrong or what I could do? It doesnt help that anything I try to fix the error doesnt work.

# pytorch code def forward(self, X):

B = torch.tensor_split(X, self.idxs, dim=3) Z = []

for i, (layer_norm, linear_layer) in enumerate(zip(self.layer_norms, self.linear_layers)): b_i = torch.cat((B[i][:, :, 0, :, :],B[i][:, :, 1, :, :]), 2) #concatenate real and imaginary spectrograms b_i = torch.transpose(layer_norm(b_i), 2, 3) #mirar be com es fa la layer norm Z.append(torch.transpose(linear_layer(b_i), 2, 3))

Z = torch.stack(Z, 3) return Z

# Tensorflow Code def call(self, inputs): B = tf.split(inputs, self.idxs.numpy(), axis=3) Z = []

for i, (layer_norm, linear_layer) in enumerate(zip(self.layer_norms, self.linear_layers)): b_i = tf.concat([B[i][:, :, :, :, 0], B[i][:, :, :, :, 1]], axis=2) b_i = tf.transpose(layer_norm(b_i), perm=[0, 1, 3, 2]) Z.append(tf.transpose(linear_layer(b_i), perm=[0, 1, 3, 2]))

Z = tf.stack(Z, axis=3) return Z

I am trying to run it on the following code, which works in pytorch, but not tensorflow:

# Test run B = 1 T = 1 C = 1 F = 1 X = tf.random.normal(shape=(B, T, C, F)) band_split = Band_Split(temporal_dimension, max_freq_idx, sample_rate, n_fft, subband_dim) result = band_split(X)

print(X.shape) # output is ([1, 2, 2, 257, 100]) print(result.shape) # output is ([1, 2, 128, 30, 100]) on pytorch, tf does not work

FieldValue
text I am trying to re write some code in tensorflow, which was originally written in pytorch, but have attempted everything, including writting my own code based on theory rather than just changing the functions from one framework to another. I have also attempted using chatgpt and it didnt give me proper results. I have written some code now but I keep getting the error mentioned above (will write the full error message in the comments). Here is both the working pytorch code and the failing tensorf…
label r/tensorflow
dataType post
communityName r/tensorflow
datetime 2023-07-17
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url_encoded Z0FBQUFBQm5Lak9FVmlBbjlVMzl4QlBQV1N6WVdqdXh2VTJHUUV3SzNXZGFJb2xhelQyaWdiYTdJdlpLZHlmNWRnVVJ0SG94cVEzSXVSWnByZE1yVXJyNU1YdEVqbVNucHFUZGxfZkk2elVDNlFRTGNHSXFtN3d4cjFRQnBHWVRqRFkxQWFyazRKVkpfbUFZblcyYVdDWE03VW9MU0ZFOUVLUGxTcmlBdlowTTE1aElnbFJsMklxeWR6aWRUbm1rdG5BTjFrRlNCdTJrcElTWEhZcEpjZnFROWJwOExrcGpoUT09

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{
  "text": "I am trying to re write some code in tensorflow, which was originally written in pytorch, but have attempted everything, including writting my own code based on theory rather than just changing the functions from one framework to another. I have also attempted using chatgpt and it didnt give me proper results. I have written some code now but I keep getting the error mentioned above (will write the full error message in the comments). Here is both the working pytorch code and the failing tensorflow code. Is there any idea of what I could be doing wrong or what I could do? It doesnt help that anything I try to fix the error doesnt work.\n\n    # pytorch code\n    def forward(self, X):\n\n        B = torch.tensor_split(X, self.idxs, dim=3)\n        Z = []\n\n        for i, (layer_norm, linear_layer) in enumerate(zip(self.layer_norms, self.linear_layers)):\n            b_i = torch.cat((B[i][:, :, 0, :, :],B[i][:, :, 1, :, :]), 2) #concatenate real and imaginary spectrograms\n            b_i = torch.transpose(layer_norm(b_i), 2, 3) #mirar be com es fa la layer norm\n            Z.append(torch.transpose(linear_layer(b_i), 2, 3))\n\n\n        Z = torch.stack(Z, 3)\n        return Z\n\n    #  Tensorflow Code\n    def call(self, inputs):\n        B = tf.split(inputs, self.idxs.numpy(), axis=3)\n        Z = []\n\n        for i, (layer_norm, linear_layer) in enumerate(zip(self.layer_norms, self.linear_layers)):\n            b_i = tf.concat([B[i][:, :, :, :, 0], B[i][:, :, :, :, 1]], axis=2)\n            b_i = tf.transpose(layer_norm(b_i), perm=[0, 1, 3, 2])\n            Z.append(tf.transpose(linear_layer(b_i), perm=[0, 1, 3, 2]))\n\n        Z = tf.stack(Z, axis=3)\n        return Z\n\n\n\nI am trying to run it on the following code, which works in pytorch, but not tensorflow:\n\n\n    # Test run\n    B = 1\n    T = 1\n    C = 1\n    F = 1\n    X = tf.random.normal(shape=(B, T, C, F))\n    band_split = Band_Split(temporal_dimension, max_freq_idx, sample_rate, n_fft, subband_dim)\n    result = band_split(X)\n\nprint(X.shape) # output is ([1, 2, 2, 257, 100])\nprint(result.shape) # output is ([1, 2, 128, 30, 100]) on pytorch, tf does not work",
  "label": "r/tensorflow",
  "dataType": "post",
  "communityName": "r/tensorflow",
  "datetime": "2023-07-17",
  "username_encoded": "Z0FBQUFBQm5LakwwTWhXRzJSZWh4b1IwSnU1Q2h1cmdZTktXNlV3ZmZGaXpDdDRVRXBHQU9KdjhmLUNrd201UGx4cHU2clY4d2lZNnF2eWdScHJVMV9tc0UwWW0wNi1qMGc9PQ==",
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}
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