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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)

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)

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)

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)

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