f02b56207713275c749ff7c0d337c49bd3dffe53,examples/simple_but_ugly/tf_models.py,MyModel,_build,#MyModel#,16

Before Change

        num_features =  images_shape[-1]
        num_classes = self.get_from_config("num_classes", 3)

        x = tf.placeholder("float", [None, num_features], name="x")
        y = tf.placeholder("int32",[None], name="y")
        y_oe = tf.one_hot(y, num_classes, name="targets")

        w = tf.Variable(tf.zeros([num_features, num_classes]))
        b = tf.Variable(tf.zeros([num_classes]))

        y_ = tf.nn.softmax(tf.matmul(x, w) + b, name="predictions")

        // Define a cost function
        //tf.losses.add_loss(tf.losses.softmax_cross_entropy(y_oe, y_))
        loss = tf.losses.softmax_cross_entropy(y_oe, y_)

After Change

        y_oe = tf.one_hot(y, num_classes, name="targets")

        c = conv2d_block(x, 32, 3, conv=dict(kernel_initializer=tf.contrib.layers.xavier_initializer()), max_pooling=dict(strides=4))
        f = flatten(c)
        f = tf.layers.dense(f, num_classes)
        y_ = tf.identity(f, name="predictions")

        // Define a cost function
        //tf.losses.add_loss(tf.losses.softmax_cross_entropy(y_oe, y_))

Instances