rfho/examples/mnist_deep_ho.py
def deepnn(x):
    """deepnn builds the graph for a deep net for classifying digits.
      Args:
        x: an input tensor with the dimensions (N_examples, 784), where 784 is the
        number of pixels in a standard MNIST image.
      Returns:
        A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values
        equal to the logits of classifying the digit into one of 10 classes (the
        digits 0-9). keep_prob is a scalar placeholder for the probability of
        dropout.
      """
    # Reshape to use within a convolutional neural net.
    # Last dimension is for "features" - there is only one here, since images are
    # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
    x_image = tf.reshape(x, [-1, 28, 28, 1])

    # First convolutional layer - maps one grayscale image to 32 feature maps.
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

    # Pooling layer - downsamples by 2X.
    h_pool1 = max_pool_2x2(h_conv1)

    # Second convolutional layer -- maps 32 feature maps to 64.
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

    # Second pooling layer.
    h_pool2 = max_pool_2x2(h_conv2)

    # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
    # is down to 7x7x64 feature maps -- maps this to 1024 features.
    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    # TODO # Dropout - controls the complexity of the model, prevents co-adaptation of
    # # features.
    # keep_prob = tf.placeholder(tf.float32)
    # h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    # Map the 1024 features to 10 classes, one for each digit
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    # RFHO: We use L2 norm weight penalty instead of dropout at the last layer.

    y_conv = tf.matmul(h_fc1, W_fc2) + b_fc2
    return y_conv, W_fc1, W_fc2
deepnn builds the graph for a deep net for classifying digits.
      Args:
        x: an input tensor with the dimensions (N_examples, 784), where 784 is the
        number of pixels in a standard MNIST image.
      Returns:
        A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values
        equal to the logits of classifying the digit into one of 10 classes (the
        digits 0-9). keep_prob is a scalar placeholder for the probability of
        dropout.
      
deepnn builds the graph for a deep net for classifying digits.
Args:
x: an input tensor with the dimensions (N_examples, 784), where 784 is the
number of pixels in a standard MNIST image.
Returns:
A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values
equal to the logits of classifying the digit into one of 10 classes (the
digits 0-9). keep_prob is a scalar placeholder for the probability of
dropout.
deepnn builds the graph for a deep net for classifying digits . Args : x : an input tensor with the dimensions ( N_examples 784 ) where 784 is the number of pixels in a standard MNIST image . Returns : A tuple ( y keep_prob ) . y is a tensor of shape ( N_examples 10 ) with values equal to the logits of classifying the digit into one of 10 classes ( the digits 0 - 9 ) . keep_prob is a scalar placeholder for the probability of dropout .
def deepnn(x):
    x_image = tf.reshape(x, [-1, 28, 28, 1])
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)
    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])
    h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    y_conv = tf.matmul(h_fc1, W_fc2) + b_fc2
    return y_conv, W_fc1, W_fc2
def deepnn ( x ) : x_image = tf . reshape ( x , [ - 1 , 28 , 28 , 1 ] ) W_conv1 = weight_variable ( [ 5 , 5 , 1 , 32 ] ) b_conv1 = bias_variable ( [ 32 ] ) h_conv1 = tf . nn . relu ( conv2d ( x_image , W_conv1 ) + b_conv1 ) h_pool1 = max_pool_2x2 ( h_conv1 ) W_conv2 = weight_variable ( [ 5 , 5 , 32 , 64 ] ) b_conv2 = bias_variable ( [ 64 ] ) h_conv2 = tf . nn . relu ( conv2d ( h_pool1 , W_conv2 ) + b_conv2 ) h_pool2 = max_pool_2x2 ( h_conv2 ) W_fc1 = weight_variable ( [ 7 * 7 * 64 , 1024 ] ) b_fc1 = bias_variable ( [ 1024 ] ) h_pool2_flat = tf . reshape ( h_pool2 , [ - 1 , 7 * 7 * 64 ] ) h_fc1 = tf . nn . relu ( tf . matmul ( h_pool2_flat , W_fc1 ) + b_fc1 ) W_fc2 = weight_variable ( [ 1024 , 10 ] ) b_fc2 = bias_variable ( [ 10 ] ) y_conv = tf . matmul ( h_fc1 , W_fc2 ) + b_fc2 return y_conv , W_fc1 , W_fc2
deepnn builds the graph for a deep net for classifying digits.
deepnn builds the graph for a deep net for classifying digits .
"""deepnn builds the graph for a deep net for classifying digits.
      Args:
        x: an input tensor with the dimensions (N_examples, 784), where 784 is the
        number of pixels in a standard MNIST image.
      Returns:
        A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values
        equal to the logits of classifying the digit into one of 10 classes (the
        digits 0-9). keep_prob is a scalar placeholder for the probability of
        dropout.
      """ # Reshape to use within a convolutional neural net. # Last dimension is for "features" - there is only one here, since images are # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc. # First convolutional layer - maps one grayscale image to 32 feature maps. # Pooling layer - downsamples by 2X. # Second convolutional layer -- maps 32 feature maps to 64. # Second pooling layer. # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image # is down to 7x7x64 feature maps -- maps this to 1024 features. # TODO # Dropout - controls the complexity of the model, prevents co-adaptation of # # features. # keep_prob = tf.placeholder(tf.float32) # h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) # Map the 1024 features to 10 classes, one for each digit # RFHO: We use L2 norm weight penalty instead of dropout at the last layer.
x