- X = tf.reshape(X, [-1, 28, 28, 1]) # first conv layer will compute 32 features for each 5×5 patch with tf.variable_scope(‘conv_layer1’): h_conv1 = learn.ops.conv2d(X, n_filters=32, filter_shape=[5, 5], bias=True, activation=tf.nn.relu) h_pool1 = max_pool_2x2(h_conv1) # second conv layer will compute 64 features for each 5×5 patch.
- Getting the best of both worlds.
- This type of model can be used for classification and regression problems.
- The last layer in the network produces the output.
- Convolution Neural Networks are unique because they’re created in mind that the input will be an image.
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Continue reading “TensorFlow in a Nutshell — Part Three: All the Models”
- Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.
- Deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features.
- In the paper, we present Wide & Deep learning—jointly trained wide linear models and deep neural networks—to combine the benefits of memorization and generalization for recommender systems.
- Deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank.
- Abstract: Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs.
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@graphific: “Combining wide linear features with deep neural nets – Paper: Code:”
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[1606.07792] Wide & Deep Learning for Recommender Systems