String representation for binary classification in tensorflow

I'm making a simple classifier to distinguish strings as hashes/not hashes:

8e9fc6968605396b74b204b1d90086cfe17c572e - hash

MySuperCoolClassWithLongNameDelegate1234 - not hash

In numerous tutorials online you can find examples of image classfifiers:

model = tf.keras.models.Sequential()

model.add(tf.keras.layers.Flatten())

model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))

model.add(tf.keras.layers.Dense(2, activation=tf.nn.softmax))



model.compile(optimizer='adam',

    loss='binary_crossentropy',

    metrics=['accuracy']

    )



model.fit(x_train, y_train, epochs=3)

Usually you load images as arrays of numbers (with or without normalizing). And it works perfectly.

But what is the best way to represent strings for such a problem?

Thanks in advance

edited Nov 19 '18 at 13:28

jdehesa

22.3k43150

asked Nov 19 '18 at 12:56

dimayak

add a comment |

I'm making a simple classifier to distinguish strings as hashes/not hashes:

8e9fc6968605396b74b204b1d90086cfe17c572e - hash

MySuperCoolClassWithLongNameDelegate1234 - not hash

In numerous tutorials online you can find examples of image classfifiers:

model = tf.keras.models.Sequential()

model.add(tf.keras.layers.Flatten())

model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))

model.add(tf.keras.layers.Dense(2, activation=tf.nn.softmax))



model.compile(optimizer='adam',

    loss='binary_crossentropy',

    metrics=['accuracy']

    )



model.fit(x_train, y_train, epochs=3)

Usually you load images as arrays of numbers (with or without normalizing). And it works perfectly.

But what is the best way to represent strings for such a problem?

Thanks in advance

edited Nov 19 '18 at 13:28

jdehesa

22.3k43150

asked Nov 19 '18 at 12:56

dimayak

add a comment |

I'm making a simple classifier to distinguish strings as hashes/not hashes:

8e9fc6968605396b74b204b1d90086cfe17c572e - hash

MySuperCoolClassWithLongNameDelegate1234 - not hash

In numerous tutorials online you can find examples of image classfifiers:

model = tf.keras.models.Sequential()

model.add(tf.keras.layers.Flatten())

model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))

model.add(tf.keras.layers.Dense(2, activation=tf.nn.softmax))



model.compile(optimizer='adam',

    loss='binary_crossentropy',

    metrics=['accuracy']

    )



model.fit(x_train, y_train, epochs=3)

Usually you load images as arrays of numbers (with or without normalizing). And it works perfectly.

But what is the best way to represent strings for such a problem?

Thanks in advance

edited Nov 19 '18 at 13:28

jdehesa

22.3k43150

asked Nov 19 '18 at 12:56

dimayak

I'm making a simple classifier to distinguish strings as hashes/not hashes:

8e9fc6968605396b74b204b1d90086cfe17c572e - hash

MySuperCoolClassWithLongNameDelegate1234 - not hash

In numerous tutorials online you can find examples of image classfifiers:

model = tf.keras.models.Sequential()

model.add(tf.keras.layers.Flatten())

model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))

model.add(tf.keras.layers.Dense(2, activation=tf.nn.softmax))



model.compile(optimizer='adam',

    loss='binary_crossentropy',

    metrics=['accuracy']

    )



model.fit(x_train, y_train, epochs=3)

Usually you load images as arrays of numbers (with or without normalizing). And it works perfectly.

But what is the best way to represent strings for such a problem?

Thanks in advance

python tensorflow classification

edited Nov 19 '18 at 13:28

jdehesa

22.3k43150

asked Nov 19 '18 at 12:56

dimayak

edited Nov 19 '18 at 13:28

jdehesa

22.3k43150

asked Nov 19 '18 at 12:56

dimayak

edited Nov 19 '18 at 13:28

jdehesa

22.3k43150

edited Nov 19 '18 at 13:28

jdehesa

22.3k43150

edited Nov 19 '18 at 13:28

jdehesa

22.3k43150

asked Nov 19 '18 at 12:56

dimayak

asked Nov 19 '18 at 12:56

dimayak

asked Nov 19 '18 at 12:56

dimayak

add a comment |

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