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0

$begingroup$

I have defined, trained and saved my tensor keras NN. Now that that is complete how do I use it output classifications to non training data?

import tensorflow as tf
import numpy as np
from tensorflow.keras import layers
from syslog import syslog_pred

model = tf.keras.Sequential()
# Adds a densely-connected layer with 64 units to the model:
model.add(layers.Dense(128, activation='relu'))
# Add another:
model.add(layers.Dense(128, activation='relu'))
# Add a softmax layer with 8 output units:
model.add(layers.Dense(8, activation='softmax'))

model.compile(optimizer=tf.train.AdamOptimizer(0.001),
 loss='categorical_crossentropy',
 metrics=['accuracy'])

model.load_weights('.my_model')

x = np.array([arr[:-1] for arr in syslog_pred], dtype=np.float32)
dataset = tf.data.Dataset.from_tensor_slices(x)

answer = model.predict(dataset, steps=30)
print(answer)

The code at the end isn't what it should be but I'm a little lost.
Any help would be appreciated!

python keras tensorflow

share|improve this question

asked 11 mins ago

Alex FAlex F

83

New contributor

Alex F is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

add a comment | 

0

$begingroup$

I have defined, trained and saved my tensor keras NN. Now that that is complete how do I use it output classifications to non training data?

import tensorflow as tf
import numpy as np
from tensorflow.keras import layers
from syslog import syslog_pred

model = tf.keras.Sequential()
# Adds a densely-connected layer with 64 units to the model:
model.add(layers.Dense(128, activation='relu'))
# Add another:
model.add(layers.Dense(128, activation='relu'))
# Add a softmax layer with 8 output units:
model.add(layers.Dense(8, activation='softmax'))

model.compile(optimizer=tf.train.AdamOptimizer(0.001),
 loss='categorical_crossentropy',
 metrics=['accuracy'])

model.load_weights('.my_model')

x = np.array([arr[:-1] for arr in syslog_pred], dtype=np.float32)
dataset = tf.data.Dataset.from_tensor_slices(x)

answer = model.predict(dataset, steps=30)
print(answer)

The code at the end isn't what it should be but I'm a little lost.
Any help would be appreciated!

python keras tensorflow

share|improve this question

asked 11 mins ago

Alex FAlex F

83

New contributor

Alex F is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

add a comment | 

$begingroup$

I have defined, trained and saved my tensor keras NN. Now that that is complete how do I use it output classifications to non training data?

import tensorflow as tf
import numpy as np
from tensorflow.keras import layers
from syslog import syslog_pred

model = tf.keras.Sequential()
# Adds a densely-connected layer with 64 units to the model:
model.add(layers.Dense(128, activation='relu'))
# Add another:
model.add(layers.Dense(128, activation='relu'))
# Add a softmax layer with 8 output units:
model.add(layers.Dense(8, activation='softmax'))

model.compile(optimizer=tf.train.AdamOptimizer(0.001),
 loss='categorical_crossentropy',
 metrics=['accuracy'])

model.load_weights('.my_model')

x = np.array([arr[:-1] for arr in syslog_pred], dtype=np.float32)
dataset = tf.data.Dataset.from_tensor_slices(x)

answer = model.predict(dataset, steps=30)
print(answer)

The code at the end isn't what it should be but I'm a little lost.
Any help would be appreciated!

python keras tensorflow

share|improve this question

asked 11 mins ago

Alex FAlex F

83

New contributor

Alex F is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

import tensorflow as tf
import numpy as np
from tensorflow.keras import layers
from syslog import syslog_pred

model = tf.keras.Sequential()
# Adds a densely-connected layer with 64 units to the model:
model.add(layers.Dense(128, activation='relu'))
# Add another:
model.add(layers.Dense(128, activation='relu'))
# Add a softmax layer with 8 output units:
model.add(layers.Dense(8, activation='softmax'))

model.compile(optimizer=tf.train.AdamOptimizer(0.001),
 loss='categorical_crossentropy',
 metrics=['accuracy'])

model.load_weights('.my_model')

x = np.array([arr[:-1] for arr in syslog_pred], dtype=np.float32)
dataset = tf.data.Dataset.from_tensor_slices(x)

answer = model.predict(dataset, steps=30)
print(answer)

share|improve this question

asked 11 mins ago

Alex FAlex F

83

New contributor

Alex F is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

share|improve this question

asked 11 mins ago

Alex FAlex F

83

New contributor

Alex F is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 11 mins ago

Alex FAlex F

83

New contributor

Alex F is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 11 mins ago

Alex FAlex F

83