julia> Pkg.clone("https://github.com/invenia/Keras.jl")

julia> ENV["PYTHON"] = ""

julia> Pkg.build("PyCall")
...

julia> Pkg.build("Keras")
...

julia> using StatsBase

julia> using Keras
Using Tensorflow backend.

julia> import Keras.Layers: Dense, Activation

julia> model = Sequential()
Keras.Sequential(PyObject <keras.models.Sequential object at 0x3186b0590>,Keras.Layer[])

julia> add!(model, Dense(80, input_dim=735))

julia> add!(model, Activation(:relu))

julia> add!(model, Dense(10))

julia> add!(model, Activation(:softmax))

julia> compile!(model; loss=:categorical_crossentropy, optimizer=:sgd, metrics=[:accuracy])

julia> h = fit!(model, rand(1000, 735), rand(1000, 10); nb_epoch=100, batch_size=32, verbose=1)
Epoch 1/100
1000/1000 [==============================] - 0s - loss: 11.5345 - acc: 0.1110
Epoch 2/100
1000/1000 [==============================] - 0s - loss: 11.5048 - acc: 0.1160
Epoch 3/100
1000/1000 [==============================] - 0s - loss: 11.4877 - acc: 0.1150
Epoch 4/100
1000/1000 [==============================] - 0s - loss: 11.4755 - acc: 0.1280
Epoch 5/100
1000/1000 [==============================] - 0s - loss: 11.4691 - acc: 0.1180
Epoch 6/100
1000/1000 [==============================] - 0s - loss: 11.4648 - acc: 0.1270
Epoch 7/100
1000/1000 [==============================] - 0s - loss: 11.4624 - acc: 0.1320
Epoch 8/100
1000/1000 [==============================] - 0s - loss: 11.4605 - acc: 0.1230
Epoch 9/100
1000/1000 [==============================] - 0s - loss: 11.4585 - acc: 0.1200
Epoch 10/100
1000/1000 [==============================] - 0s - loss: 11.4571 - acc: 0.1210
Epoch 11/100
1000/1000 [==============================] - 0s - loss: 11.4557 - acc: 0.1220
Epoch 12/100
1000/1000 [==============================] - 0s - loss: 11.4539 - acc: 0.1280
Epoch 13/100
1000/1000 [==============================] - 0s - loss: 11.4528 - acc: 0.1350
Epoch 14/100
1000/1000 [==============================] - 0s - loss: 11.4512 - acc: 0.1220
Epoch 15/100
1000/1000 [==============================] - 0s - loss: 11.4500 - acc: 0.1330
Epoch 16/100
1000/1000 [==============================] - 0s - loss: 11.4484 - acc: 0.1370
Epoch 17/100
1000/1000 [==============================] - 0s - loss: 11.4472 - acc: 0.1340
Epoch 18/100
1000/1000 [==============================] - 0s - loss: 11.4456 - acc: 0.1350
Epoch 19/100
1000/1000 [==============================] - 0s - loss: 11.4446 - acc: 0.1330
Epoch 20/100
1000/1000 [==============================] - 0s - loss: 11.4430 - acc: 0.1400
Epoch 21/100
1000/1000 [==============================] - 0s - loss: 11.4418 - acc: 0.1410
Epoch 22/100
1000/1000 [==============================] - 0s - loss: 11.4404 - acc: 0.1410
Epoch 23/100
1000/1000 [==============================] - 0s - loss: 11.4389 - acc: 0.1430
Epoch 24/100
1000/1000 [==============================] - 0s - loss: 11.4380 - acc: 0.1460
Epoch 25/100
...
Epoch 100/100
1000/1000 [==============================] - 0s - loss: 11.3092 - acc: 0.2580
PyObject <keras.callbacks.History object at 0x322cd7f10>

julia> evaluate(model, rand(10, 735), rand(10, 10); batch_size=5, verbose=1)
 5/10 [==============>...............] - ETA: 0s2-element Array{Any,1}:
 12.4966
  0.2

julia> predict(model, rand(10, 735); batch_size=5, verbose=1)
5/10 [==============>...............] - ETA: 0s10×10 Array{Float32,2}:
0.100348   0.0992692  0.0923158  0.0851037  0.103129   0.106778   0.11058    0.101286   0.0894175  0.111771
0.0976726  0.0875343  0.108842   0.0852642  0.0820421  0.106634   0.105027   0.101865   0.121497   0.103621
0.0598381  0.10897    0.0837665  0.0733929  0.0725107  0.122981   0.162031   0.144932   0.0795453  0.0920316
0.131148   0.124512   0.087671   0.116357   0.087715   0.085439   0.0887628  0.0977076  0.102328   0.0783594
0.135756   0.131731   0.135302   0.0765197  0.0982455  0.0807826  0.0835469  0.0812529  0.113327   0.0635366
0.0944178  0.072396   0.142516   0.0971909  0.107448   0.0802704  0.0959812  0.113621   0.0858358  0.110323
0.101508   0.102114   0.0758901  0.128942   0.114254   0.110218   0.0886269  0.0907067  0.113456   0.0742862
0.113782   0.0607797  0.101801   0.0774414  0.0919279  0.0721519  0.127951   0.0911597  0.106482   0.156523
0.0964221  0.0636331  0.0747976  0.0916739  0.0871047  0.117788   0.101153   0.133917   0.106753   0.126758
0.131108   0.074069   0.0929551  0.0934865  0.0924049  0.0769063  0.111134   0.115697   0.12413    0.0881087

julia> Pkg.test("Keras")

help?> Keras.Layers.Dense
Just your regular fully connected NN layer.

    # Example

    ```python
        # as first layer in a sequential model:
        model = Sequential()
        model.add(Dense(32, input_dim=16))
        # now the model will take as input arrays of shape (*, 16)
        # and output arrays of shape (*, 32)

        # this is equivalent to the above:
        model = Sequential()
        model.add(Dense(32, input_shape=(16,)))

        # after the first layer, you don't need to specify
        # the size of the input anymore:
        model.add(Dense(32))
    ```

    # Arguments
        output_dim: int > 0.
        init: name of initialization function for the weights of the layer
            (see [initializations](../initializations.md)),
            or alternatively, Theano function to use for weights
            initialization. This parameter is only relevant
            if you don't pass a `weights` argument.
        activation: name of activation function to use
            (see [activations](../activations.md)),
            or alternatively, elementwise Theano function.
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: a(x) = x).
        weights: list of Numpy arrays to set as initial weights.
            The list should have 2 elements, of shape `(input_dim, output_dim)`
            and (output_dim,) for weights and biases respectively.
        W_regularizer: instance of [WeightRegularizer](../regularizers.md)
            (eg. L1 or L2 regularization), applied to the main weights matrix.
        b_regularizer: instance of [WeightRegularizer](../regularizers.md),
            applied to the bias.
        activity_regularizer: instance of [ActivityRegularizer](../regularizers.md),
            applied to the network output.
        W_constraint: instance of the [constraints](../constraints.md) module
            (eg. maxnorm, nonneg), applied to the main weights matrix.
        b_constraint: instance of the [constraints](../constraints.md) module,
            applied to the bias.
        bias: whether to include a bias (i.e. make the layer affine rather than linear).
        input_dim: dimensionality of the input (integer).
            This argument (or alternatively, the keyword argument `input_shape`)
            is required when using this layer as the first layer in a model.

    # Input shape
        2D tensor with shape: `(nb_samples, input_dim)`.

    # Output shape
        2D tensor with shape: `(nb_samples, output_dim)`.