add deep net in keras

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# Deep Neural Network in Keras

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In this notebook, we build a Deep Neural Network while discussing the theory we've learnt thus far.

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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.layers import Dropout # new!

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()

```

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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

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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#### Design neural network architecture

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``` python

model = Sequential()

model.add(Dense(64, activation='sigmoid', input_shape=(784,)))

model.add(Dense(10, activation='softmax'))

```

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``` python

model.summary()

```

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#### Configure model

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``` python

model.compile(loss='mean_squared_error', optimizer=SGD(lr=0.01), metrics=['accuracy'])

```

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#### Train!

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``` python

model.fit(X_train, y_train, batch_size=128, epochs=200, verbose=1, validation_data=(X_test, y_test))

```

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``` python

```