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pok.py
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pok.py
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import numpy as np
import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
data_train = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
data_test= tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
X_train=data_train.flow_from_directory(
'data/training_set',
target_size=(64,64),
batch_size=64,
class_mode='binary')
x_test=data_test.flow_from_directory(
'data/test_set',
target_size=(64,64),
batch_size=64,
class_mode='binary')
model=tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(32,(3,3),input_shape=(64,64,3),activation="relu"))
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))
model.add(tf.keras.layers.Conv2D(32,(3,3),activation="relu"))
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))
model.add(tf.keras.layers.Conv2D(64,(3,3),activation="relu"))
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128,activation="relu"))
model.add(tf.keras.layers.Dense(128,activation="relu"))
model.add(tf.keras.layers.Dense(1,activation="sigmoid"))
model.compile(optimizer="adam",loss="binary_crossentropy",metrics=["accuracy"])
model.fit_generator(
X_train,
#steps_per_epoch=2000,
epochs=13,
validation_data=x_test
#validation_steps=800
)