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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Conv layer #1 
Step1:  데이터 로드
Step2:  Create Estimator
Step3:  Train model
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Python Code:
def cnn_model_fn(features, labels, mode):
    input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
    
    conv1 = tf.layers.conv2d(
            inputs=input_layer,
            filters=32, 
            kernel_size=[5, 5],
            padding="same",
            activation=tf.nn.relu)
    
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2],
                                   strides=2)
    
    conv2 = tf.layers.conv2d(inputs=pool1, filters=64, kernel_size=[5, 5],
                            padding="same", activation=tf.nn.relu)
    
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], 
                                   strides=2)
    
    pool2_float = tf.reshape(pool2, [-1, 7 * 7 * 64])
    dense = tf.layers.dense(inputs=pool2_float, units=1024, activation=tf.nn.relu)
    
    dropout = tf.layers.dropout(
            inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
    
    # tf.estimator.ModeKeys.TRAIN은 torch의 model.eval()이런식으로 모드 설정하는 거인듯
    
    logits = tf.layers.dense(inputs=dropout, units=10)
    
    predictions = {
        "classes" : tf.argmax(input=logits, axis=1),
        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
    }
    
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
    
    # Calculate Loss
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
    
    # Configure the Training Op
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
        train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
    
    # Add evaluation metrics
    eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(labels=labels, predictions=predictions["classes"])
    }
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
Explanation: Conv layer #1 : 32 5x5 filters, ReLU
Pooling layer #1 : 2x2 filter, stride 2
Conv layer #2 : 64 5x5, ReLU
Pooling layer #2 : 2x2 filter, stride 2
Dense Layer #1 : 1024, with dropout regularization rate of 0.4
Dense layer #2(logit) : 10 neurons, one for each digit target class
tf.layer module
conv2d()
max_pooling2d()
dense()
End of explanation
mnist = tf.contrib.learn.datasets.load_dataset("mnist")
train_data = mnist.train.images
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
eval_data = mnist.test.images 
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
Explanation: 데이터 로드
End of explanation
mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn,
                                         model_dir="./tmp/mnist_convnet")
tensors_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=50)
Explanation: Create Estimator
End of explanation
%%time
train_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"x":train_data},
    y=train_labels,
    batch_size=100,
    num_epochs=None,
    shuffle=True)
mnist_classifier.train(input_fn=train_input_fn, steps=500, hooks=[logging_hook])
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"x": eval_data},
    y=eval_labels,
    num_epochs=1,
    shuffle=False)
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)
Explanation: Train model
End of explanation
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