MNIST loaded locally

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# Shallow Neural Network in Keras

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Build a shallow neural network to classify MNIST digits

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#### Set seed for reproducibility

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``` python

import numpy as np

np.random.seed(42)

```

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#### Load dependencies

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``` python

import keras

from keras.datasets import mnist

from keras.models import Sequential

from keras.layers import Dense

from keras.optimizers import SGD

```

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#### Load data

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``` python

(X_train, y_train), (X_test, y_test) = mnist.load_data()

#(X_train, y_train), (X_test, y_test) = mnist.load_data()

import pickle

X_train = pickle.load(open('../data/mnist_X_train.pkl', 'rb'))

y_train = pickle.load(open('../data/mnist_y_train.pkl', 'rb'))

X_test = pickle.load(open('../data/mnist_X_test.pkl', 'rb'))

y_test = pickle.load(open('../data/mnist_y_test.pkl', 'rb'))

```

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``` python

X_train.shape

```

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``` python

y_train.shape

```

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``` python

y_train[0:99]

```

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``` python

X_test.shape

```

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``` python

X_test[0]

```

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``` python

y_test.shape

```

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``` python

y_test[0]

```

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#### Preprocess data

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``` python

X_train = X_train.reshape(60000, 784).astype('float32')

X_test = X_test.reshape(10000, 784).astype('float32')

```

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``` python

X_train /= 255

X_test /= 255

```

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``` python

X_test[0]

```

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``` python

n_classes = 10

y_train = keras.utils.to_categorical(y_train, n_classes)

y_test = keras.utils.to_categorical(y_test, n_classes)

```

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``` python

y_test[0]

```

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#### Design neural network architecture

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``` python

# add architecture here

```

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``` python

model.summary()

```

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``` python

(64*784)

```

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``` python

(64*784)+64

```

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``` python

(10*64)+10

```

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#### Configure model

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``` python

# compile model here

```

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#### Train!

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``` python

# train model here

```

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``` python

model.evaluate(X_test, y_test)

```

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``` python

```