Row 2221

Row ID: 2221 | Dataset Entry | Axioma AXP Content Repository

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""" from tensorflow.keras.preprocessing.sequence import pad\_sequences import tensorflow as tf

maxlen = 500

X\_train = pad\_sequences(X\_train, maxlen=maxlen, padding='post', truncating='post') X\_test = pad\_sequences(X\_test, maxlen=maxlen, padding='post', truncating='post') X\_train\_reshaped = X\_train.reshape((\*X\_train.shape, 1)) X\_test\_reshaped = X\_test.reshape((\*X\_test.shape, 1))

model = Sequential() model.add(LSTM(128)) model.add(Dense(1, activation="sigmoid")) model.compile(optimizer='adam', loss='binary\_crossentropy') #model.summary() model.fit(X\_train, y\_train, epochs=10, validation\_data=(X\_test, y\_test), verbose=2)

"""

I keep getting the error and I am not sure what I am doing wrong as I nearly copied the same exact example as the code shown in this website([https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/](https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/))

Call arguments received by layer "sequential" " f"(type Sequential): • inputs=tf.Tensor(shape=(None, 500), dtype=int32) • training=True • mask=None

FieldValue
text """ from tensorflow.keras.preprocessing.sequence import pad\_sequences import tensorflow as tf maxlen = 500 X\_train = pad\_sequences(X\_train, maxlen=maxlen, padding='post', truncating='post') X\_test = pad\_sequences(X\_test, maxlen=maxlen, padding='post', truncating='post') X\_train\_reshaped = X\_train.reshape((\*X\_train.shape, 1)) X\_test\_reshaped = X\_test.reshape((\*X\_test.shape, 1)) model = Sequential() model.add(LSTM(128)) model.add(Dense(1, activation="sigmoid")) model.com…
label r/tensorflow
dataType post
communityName r/tensorflow
datetime 2023-07-19
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url_encoded Z0FBQUFBQm5Lak9FRTdUVjVHQVN1Q1NUYW04UlhGR1FjaFpGVGs5QWFXb0Z5MUNaV1hRb3FCa21GOHlvbTkwYzhuck8zVF9vd3I0b1F4eExwa1JDd3ZjYWtVejFRYWg5T2ltRHJZWEQtcHhzeWx5MS1neXRkSXBtZDJPVThJNWpmNWV0amRkWmVCcnU1ZHNEVDRhc0JDNXQxSmFWekJpOTRGR2pjYk0tdFF4ZUwwbXg1MUdCZTlsdURLck1aUGxOVXNMd0c0R3F4TU55ckV3WVRmRjdQRGU0bk5lV0I4VVh0UT09

Raw Record

{
  "text": "\"\"\"   \nfrom tensorflow.keras.preprocessing.sequence import pad\\_sequences import tensorflow as tf   \n\n\nmaxlen = 500\n\nX\\_train = pad\\_sequences(X\\_train, maxlen=maxlen, padding='post', truncating='post') X\\_test = pad\\_sequences(X\\_test, maxlen=maxlen, padding='post', truncating='post') X\\_train\\_reshaped = X\\_train.reshape((\\*X\\_train.shape, 1)) X\\_test\\_reshaped = X\\_test.reshape((\\*X\\_test.shape, 1))\n\nmodel = Sequential() model.add(LSTM(128)) model.add(Dense(1, activation=\"sigmoid\")) model.compile(optimizer='adam', loss='binary\\_crossentropy') #model.summary() model.fit(X\\_train, y\\_train, epochs=10, validation\\_data=(X\\_test, y\\_test), verbose=2)\n\n \"\"\"\n\nI keep getting the error and I am not sure what I am doing wrong as I nearly copied the same exact example as the code shown in this website([https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/](https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/))\n\nCall arguments received by layer \"sequential\" \"                 f\"(type Sequential):       • inputs=tf.Tensor(shape=(None, 500), dtype=int32)       • training=True       • mask=None",
  "label": "r/tensorflow",
  "dataType": "post",
  "communityName": "r/tensorflow",
  "datetime": "2023-07-19",
  "username_encoded": "Z0FBQUFBQm5LakwwNXlEeUt0S2x4UFhxOGk4OGhRTmpHLVVmTGt1U01BTFB1Q0dqWXhCN09EQkN4ZXoxb1R0WnZ5M1ZaOVRLaFBFaEsxS3VqZ1pQczlKZlVwb01UTm1rZFE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9FRTdUVjVHQVN1Q1NUYW04UlhGR1FjaFpGVGs5QWFXb0Z5MUNaV1hRb3FCa21GOHlvbTkwYzhuck8zVF9vd3I0b1F4eExwa1JDd3ZjYWtVejFRYWg5T2ltRHJZWEQtcHhzeWx5MS1neXRkSXBtZDJPVThJNWpmNWV0amRkWmVCcnU1ZHNEVDRhc0JDNXQxSmFWekJpOTRGR2pjYk0tdFF4ZUwwbXg1MUdCZTlsdURLck1aUGxOVXNMd0c0R3F4TU55ckV3WVRmRjdQRGU0bk5lV0I4VVh0UT09"
}
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