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+import datetime
+import numpy as np
+import matplotlib.pyplot as plt
+from keras.layers import Input, Activation, Conv2D, Flatten, Dense, MaxPooling2D
+from keras.models import Model, load_model
+from keras.preprocessing.image import ImageDataGenerator
+from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
+plt.style.use('dark_background')
+
+
+x_train = np.load('sauce/x_train.npy').astype(np.float32)
+y_train = np.load('sauce/y_train.npy').astype(np.float32)
+x_val = np.load('sauce/x_val.npy').astype(np.float32)
+y_val = np.load('sauce/y_val.npy').astype(np.float32)
+
+
+'''
+print(x_train.shape,y_train.shape)
+(2586,26,34,1)(2586,1) 
+세로 26 가로34  흑백 이미지 2586개, 
+
+print(x_val.shape,y_val.shape)
+(288, 26, 34, 1) (288, 1)
+'''
+
+
+train_datagen = ImageDataGenerator(
+    #data Augmentation 데이터변형 데이터 수 늘리기
+    rescale=1./255,
+    rotation_range=10,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    shear_range=0.2
+)
+val_datagen = ImageDataGenerator(
+    rescale=1. / 255,
+    rotation_range=10,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    shear_range=0.2
+)
+train_generator = train_datagen.flow(
+    #원 데이터셋에 ImageDataGenerator 이용한 변형한 것을 적용
+    x=x_train, y=y_train,
+    batch_size=32,
+    shuffle=True
+)
+val_generator = val_datagen.flow(
+    x=x_val, y=y_val,
+    batch_size=32,
+    shuffle=False
+)
+
+inputs = Input(shape=(26, 34, 1))
+net = Conv2D(32, kernel_size=3, strides=1, padding='same', activation='relu')(inputs)
+#1번째 convolution layer
+net = MaxPooling2D(pool_size=2)(net)
+#데이터 연산 줄여주는 역할
+#여기서는 연산 후 가장 큰 값 MaxPooling
+#차원 축소
+net = Conv2D(64, kernel_size=3, strides=1, padding='same', activation='relu')(net)
+net = MaxPooling2D(pool_size=2)(net)
+net = Conv2D(128, kernel_size=3, strides=1, padding='same', activation='relu')(net)
+net = MaxPooling2D(pool_size=2)(net)
+net = Flatten()(net)
+#완전연결층
+#1차원으로 펴주고
+net = Dense(512)(net)
+#512개의 fully connected layer와 연결
+net = Activation('relu')(net)
+net = Dense(1)(net)
+#마지막 레이어 한개 0~1사이값
+outputs = Activation('sigmoid')(net)
+#sigmoid 0~1
+model = Model(inputs=inputs, outputs=outputs)
+model.compile(optimizer='Nadam', loss='binary_crossentropy', metrics=['acc'])
+#loss는 0이냐 1이냐 니깐 binary, accuracy 추가
+
+model.summary()
+
+model.fit_generator(
+    #위의 generator로 데이터셋 늘렸기에 이때 는 fit_generator가 좋다
+    train_generator, epochs=50, validation_data=val_generator,
+    callbacks=[
+        #save_best_only 학습이 잘되면 저장해라
+        ModelCheckpoint('sauce/models.h5', monitor='val_acc', save_best_only=True, mode='max', verbose=1),
+
+        #learning이 잘안되면 learning rate를 줄여라
+        ReduceLROnPlateau(monitor='val_acc', factor=0.2, patience=10, verbose=1, mode='auto', min_lr=1e-05)
+    ]
+)
+
+
+
+
+