TensorFlow培训

假设我有一个非常简单的神经网络,如多层感知器.对于每一层,激活功能是S形的,并且网络是完全连接的.

在TensorFlow中,这可能是这样定义的:

    sess = tf.InteractiveSession()

    # Training Tensor
    x = tf.placeholder(tf.float32, shape = [None, n_fft])
    # Label Tensor
    y_ = tf.placeholder(tf.float32, shape = [None, n_fft])

    # Declaring variable buffer for weights W and bias b
    # Layer structure [n_fft, n_fft, n_fft, n_fft]
    # Input -> Layer 1
    struct_w = [n_fft, n_fft]
    struct_b = [n_fft]
    W1 = weight_variable(struct_w, 'W1')
    b1 = bias_variable(struct_b, 'b1')
    h1 = tf.nn.sigmoid(tf.matmul(x, W1) + b1)

    # Layer1 -> Layer 2
    W2 = weight_variable(struct_w, 'W2')
    b2 = bias_variable(struct_b, 'b2')
    h2 = tf.nn.sigmoid(tf.matmul(h1, W2) + b2)

    # Layer2 -> output
    W3 = weight_variable(struct_w, 'W3')
    b3 = bias_variable(struct_b, 'b3')
    y = tf.nn.sigmoid(tf.matmul(h2, W3) + b3)

    # Calculating difference between label and output using mean square error
    mse = tf.reduce_mean(tf.square(y - y_))

    # Train the Model
    # Gradient Descent
    train_step = tf.train.GradientDescentOptimizer(0.3).minimize(mse)

for i in xrange(200):
        train_step.run(feed_dict = {x: spec_train, y_: spec_target})

for i in xrange(200):
        for j in xrange(3000):
            train = spec_train[j, :].reshape(1, n_fft)
            label = spec_target[j, :].reshape(1, n_fft)
            train_step.run(feed_dict = {x: train, y_: label})