import tensorflow as tf
from tensorflow.keras.layers import *
import numpy as np
import img_loader
x_train, y_train = img_loader.load_data("../flower_photos")
x_train = x_train / 255.0
model = tf.keras.Sequential()
model.add(Conv2D(100, kernel_size=(5,5), activation='relu', input_shape=(224,224,3)))
model.add(MaxPooling2D(pool_size=(3,3)))
model.add(Conv2D(100, kernel_size=(4,4), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(5, activation='softmax'))
model.compile(optimizer='adam',
loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=['accuracy'])
model.fit(x=x_train, y=y_train, batch_size=50, epochs=20)
model.save("model3.h5")
import tensorflow as tf
from tensorflow.keras.layers import *
import numpy as np
import img_loader
x_train, y_train = img_loader.load_data("../dogs-vs-cats/train")
x_train = x_train / 255.0
model = tf.keras.Sequential()
model.add(
Conv2D(32,
kernel_size=(3, 3),
activation='relu',
input_shape=(224, 224, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
model.compile(optimizer='rmsprop',
loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=['accuracy'])
