human
I am working on the file ".ipynb_checkpoints/Diploma_munka_notebook_0-checkpoint.ipynb".
The first task is:
Simple convolutional network, stride=2
Can you write Python program for it?
 ... Give me a the Python program you requested?
assistant
#Here is the Python program you requested.

depth = 16
graph = tf.Graph()

with graph.as_default():

    # Input data.
    tf_train_dataset = tf.placeholder(tf.float32, shape=(batch_size, image_size, image_size, num_channels))
    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))

    tf_valid_dataset = tf.constant(valid_dataset_conv)
    tf_test_dataset = tf.constant(test_dataset_conv)

    # Variables.
    layer1_weights = tf.Variable(tf.truncated_normal([patch_size, patch_size, num_channels, depth], stddev=0.1))
    layer1_biases = tf.Variable(tf.zeros([depth]))

    layer2_weights = tf.Variable(tf.truncated_normal([patch_size, patch_size, depth, depth], stddev=0.1))
    layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))

    layer3_weights = tf.Variable(tf.truncated_normal([image_size // 4 * image_size // 4 * depth, num_hidden], 
                                                    stddev=0.1))
    layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))

    layer4_weights = tf.Variable(tf.truncated_normal([num_hidden, num_labels], stddev=0.1))
    layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))

    # Model.
    def model(data):
        conv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding='SAME')
        hidden = tf.nn.relu(conv + layer1_biases)

        conv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding='SAME')
        hidden = tf.nn.relu(conv + layer2_biases)

        shape = hidden.get_shape().as_list()
        reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])

        hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)
        return tf.matmul(hidden, layer4_weights) + layer4_biases

    # Training computation.
    logits = model(tf_train_dataset)
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))

    # Optimizer.
    optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)

    # Predictions for the training, validation, and test data.
    train_prediction =...