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import tensorflow as tf
from keras import Sequential
from keras.layers import Conv2D
model = Sequential()
model.add(Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)))

model.summary()

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# Set dropout rate
DROPOUT = 0.1 # Drop 10%

# Build model
model = tf.keras.Sequential()
model.add(keras.layers.Dense(HIDDEN_NEURON, input_shape=(RESHAPED, ),
                             name='dense_layer', activation='relu'))
model.add(keras.layers.Dropout(DROPOUT))
model.add(keras.layers.Dense(HIDDEN_NEURON,
                             name='dense_layer_2', activation='relu'))

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EPOCHS = 300
BATCH_SIZE = 128
VERBOSE = 1
NB_CLASSES = 10 
HIDDEN_NEURON = 128
VALIDATION_SPLIT = 0.2

# Build model
from keras import Sequential
from keras.layers import Dense

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%tensorboard --logdir drive/MyDrive/logs/tf_simple

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# Model evaluation 
test_loss, test_acc = model.evaluate(X_test, Y_test)
print('Test accuracy:', test_acc)

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# Model train
# Create new TensorBoard session everytime we train a model
tensorboard = create_tensorboard_callback()
model.fit(X_train, Y_train, batch_size=BATCH_SIZE,
          epochs=EPOCHS, verbose=VERBOSE, validation_split=VALIDATION_SPLIT,callbacks=[tensorboard])

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# Model compile
model.compile(optimizer='SGD',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.summary()

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# Build model
from keras import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(NB_CLASSES, input_shape=(RESHAPED, ),
                name='dense_layer', activation='softmax'))

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%load_ext tensorboard
import datetime

# Create a function to build a TensorBoard callback
def create_tensorboard_callback():
    # Create a log directory for storing TensorBoard logs
    logdir = os.path.join('drive/MyDrive/logs/tf_simple',
                          # Make it so the logs get tracked whenever we run an experiment
                          datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
    return tf.keras.callbacks.TensorBoard(logdir)

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import tensorflow as tf
import numpy as np
import os
from tensorflow import keras

# Neural network and train variables
EPOCHS = 200
BATCH_SIZE = 128
VERBOSE = 1
NB_CLASSES = 10 # Num of outputs