How to use the cgt.nn.dropout function in cgt

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github joschu / cgt / examples / demo_mnist.py View on Github external
def dense_model(X, w_h, w_h2, w_o, p_drop_input, p_drop_hidden):
    X = nn.dropout(X, p_drop_input)
    h = nn.rectify(cgt.dot(X, w_h))

    h = nn.dropout(h, p_drop_hidden)
    h2 = nn.rectify(cgt.dot(h, w_h2))

    h2 = nn.dropout(h2, p_drop_hidden)
    py_x = nn.softmax(cgt.dot(h2, w_o))
    return py_x
github joschu / cgt / examples / demo_mnist.py View on Github external
def dense_model(X, w_h, w_h2, w_o, p_drop_input, p_drop_hidden):
    X = nn.dropout(X, p_drop_input)
    h = nn.rectify(cgt.dot(X, w_h))

    h = nn.dropout(h, p_drop_hidden)
    h2 = nn.rectify(cgt.dot(h, w_h2))

    h2 = nn.dropout(h2, p_drop_hidden)
    py_x = nn.softmax(cgt.dot(h2, w_o))
    return py_x
github joschu / cgt / examples / demo_mnist.py View on Github external
def convnet_model(X, w, w2, w3, w4, w_o, p_drop_conv, p_drop_hidden):
    l1a = nn.rectify(nn.conv2d(X, w, kernelshape=(3,3), pad=(1,1)))
    l1 = nn.max_pool_2d(l1a, kernelshape=(2, 2), stride=(2,2))
    l1 = nn.dropout(l1, p_drop_conv)

    l2a = nn.rectify(nn.conv2d(l1, w2, kernelshape=(3,3), pad=(1,1)))
    l2 = nn.max_pool_2d(l2a, kernelshape=(2, 2), stride=(2,2))
    l2 = nn.dropout(l2, p_drop_conv)

    l3a = nn.rectify(nn.conv2d(l2, w3, kernelshape=(3,3), pad=(1,1)))
    l3b = nn.max_pool_2d(l3a, kernelshape=(2, 2), stride=(2,2))
    batchsize,channels,rows,cols = l3b.shape
    l3 = cgt.reshape(l3b, [batchsize, channels*rows*cols])
    l3 = nn.dropout(l3, p_drop_conv)

    l4 = nn.rectify(cgt.dot(l3, w4))
    l4 = nn.dropout(l4, p_drop_hidden)
    
    pyx = nn.softmax(cgt.dot(l4, w_o))
    return pyx
github joschu / cgt / examples / demo_mnist.py View on Github external
l1a = nn.rectify(nn.conv2d(X, w, kernelshape=(3,3), pad=(1,1)))
    l1 = nn.max_pool_2d(l1a, kernelshape=(2, 2), stride=(2,2))
    l1 = nn.dropout(l1, p_drop_conv)

    l2a = nn.rectify(nn.conv2d(l1, w2, kernelshape=(3,3), pad=(1,1)))
    l2 = nn.max_pool_2d(l2a, kernelshape=(2, 2), stride=(2,2))
    l2 = nn.dropout(l2, p_drop_conv)

    l3a = nn.rectify(nn.conv2d(l2, w3, kernelshape=(3,3), pad=(1,1)))
    l3b = nn.max_pool_2d(l3a, kernelshape=(2, 2), stride=(2,2))
    batchsize,channels,rows,cols = l3b.shape
    l3 = cgt.reshape(l3b, [batchsize, channels*rows*cols])
    l3 = nn.dropout(l3, p_drop_conv)

    l4 = nn.rectify(cgt.dot(l3, w4))
    l4 = nn.dropout(l4, p_drop_hidden)
    
    pyx = nn.softmax(cgt.dot(l4, w_o))
    return pyx
github joschu / cgt / examples / demo_mnist.py View on Github external
def dense_model(X, w_h, w_h2, w_o, p_drop_input, p_drop_hidden):
    X = nn.dropout(X, p_drop_input)
    h = nn.rectify(cgt.dot(X, w_h))

    h = nn.dropout(h, p_drop_hidden)
    h2 = nn.rectify(cgt.dot(h, w_h2))

    h2 = nn.dropout(h2, p_drop_hidden)
    py_x = nn.softmax(cgt.dot(h2, w_o))
    return py_x
github joschu / cgt / examples / demo_mnist.py View on Github external
def convnet_model(X, w, w2, w3, w4, w_o, p_drop_conv, p_drop_hidden):
    l1a = nn.rectify(nn.conv2d(X, w, kernelshape=(3,3), pad=(1,1)))
    l1 = nn.max_pool_2d(l1a, kernelshape=(2, 2), stride=(2,2))
    l1 = nn.dropout(l1, p_drop_conv)

    l2a = nn.rectify(nn.conv2d(l1, w2, kernelshape=(3,3), pad=(1,1)))
    l2 = nn.max_pool_2d(l2a, kernelshape=(2, 2), stride=(2,2))
    l2 = nn.dropout(l2, p_drop_conv)

    l3a = nn.rectify(nn.conv2d(l2, w3, kernelshape=(3,3), pad=(1,1)))
    l3b = nn.max_pool_2d(l3a, kernelshape=(2, 2), stride=(2,2))
    batchsize,channels,rows,cols = l3b.shape
    l3 = cgt.reshape(l3b, [batchsize, channels*rows*cols])
    l3 = nn.dropout(l3, p_drop_conv)

    l4 = nn.rectify(cgt.dot(l3, w4))
    l4 = nn.dropout(l4, p_drop_hidden)
    
    pyx = nn.softmax(cgt.dot(l4, w_o))
    return pyx