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train_data.py
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train_data.py
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# Created by:
# Name : Alan Fhajoeng Ramadhan
# Country : Indonesia
# email : alfhatech.id@gmail.com
# 21 March 2020
# usage : python train_data.py
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import TensorBoard
import pickle
import time
pickle_in = open("X.pickle", "rb")
X = pickle.load(pickle_in)
pickle_in = open("y.pickle", "rb")
y = pickle.load(pickle_in)
X = X / 255.0
dense_layers = [0]
layer_sizes = [32]
conv_layers = [2]
for dense_layer in dense_layers:
for layer_size in layer_sizes:
for conv_layer in conv_layers:
NAME = "{}-conv-{}-nodes-{}-dense-{}".format(conv_layer, layer_size, dense_layer, int(time.time()))
tensorboard = TensorBoard(log_dir= r'Your path\logs\{}'.format(NAME)) # make your logs directory path
print(NAME)
model = Sequential()
model.add(Conv2D(layer_size, (3, 3), input_shape=X.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
for l in range(conv_layer-1):
model.add(Conv2D(layer_size, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
for j in range(dense_layer):
model.add(Dense(layer_size))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X, y, batch_size=32, epochs=20, validation_split=0.2, callbacks=[tensorboard])
model.save('32x2x0-CNN.model')
# Delete this if you run it
Created by:
Name : Alan Fhajoeng Ramadhan
From : Indonesia
email : alfhatech.id@gmail.com
21 March 2020
usage : python train_data.py