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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/akasaa101/API-Security-Checklist/blob/master/mnist_dataset_0_9874.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

# + id="9IErrKYBBQdT" colab_type="code" colab={}
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

# + id="pcBy5khQ8PC9" colab_type="code" colab={}
batch_size=128
num_classes=10
epoch=12

# + id="eq4Z1o088XRL" colab_type="code" outputId="7cbf0e57-81a9-4978-ec80-c2b6149e7ac2" colab={"base_uri": "https://localhost:8080/", "height": 52}
#input image dimensions
img_rows, img_cols=28,28

#the data, split between train and test
(x_train,y_train),(x_test,y_test)=mnist.load_data()



# + id="cXGepaWI8z6L" colab_type="code" colab={}
if K.image_data_format() == 'channels_first':
  x_train= x_train.reshape(x_train.shape[0],1,img_rows,img_cols)
  x_test=x_test.reshape(x_test.shape[0],1,img_rows,img_cols)
  input_shape = (1,img_rows,img_cols)
else:
  x_train=x_train.reshape(x_train.shape[0],img_rows,img_cols,1)
  x_test=x_test.reshape(x_test.shape[0],img_rows,img_cols,1)
  input_shape=(img_rows,img_cols,1)
  


# + id="bkQSbjlb92OY" colab_type="code" outputId="e560389e-bf5a-4c54-d957-d561913253d9" colab={"base_uri": "https://localhost:8080/", "height": 69}
x_train=x_train.astype('float32')
x_test=x_test.astype('float32')
x_train /=255
x_test /=255
print('x_train shape: ' ,x_train.shape)
print(x_train.shape[0],' train samples')
print(x_test.shape[0],' test samples')

# + id="0dFXn5O0-ltM" colab_type="code" colab={}
#convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train,num_classes)
y_test = keras.utils.to_categorical(y_test,num_classes)

# + id="-oAD2Qmh-7Wy" colab_type="code" outputId="66c429ad-2226-457d-c458-fac3bc53ebe8" colab={"base_uri": "https://localhost:8080/", "height": 141}
model=Sequential()
model.add(Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes,activation='softmax'))


# + id="Fkv4dVkJ_1k3" colab_type="code" colab={}
model.compile(loss=keras.losses.categorical_crossentropy,
             optimizer=keras.optimizers.Adadelta(),
             metrics=['accuracy'])

# + id="kggpjwuEAK8x" colab_type="code" outputId="860d38c7-6d9f-44d4-b083-d6a0c0658541" colab={"base_uri": "https://localhost:8080/", "height": 541}
model.fit(x_train,y_train,
         batch_size=batch_size,
         epochs=12,
         verbose=1,
         validation_data=(x_test,y_test))

# + id="ewUDeI8MCk7e" colab_type="code" colab={}
score=model.evaluate(x_test,y_test,verbose=0)


# + id="q8sqnG5tCup9" colab_type="code" outputId="b434bc7a-ebe4-4fff-9f32-3dceb1d84e35" colab={"base_uri": "https://localhost:8080/", "height": 52}
print('Test loss: ', score[0])
print('Test accuracy: ', score[1])

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