diff --git a/load_model_mnist_keras.py b/load_model_mnist_keras.py
index 42c7523..0853625 100644
--- a/load_model_mnist_keras.py
+++ b/load_model_mnist_keras.py
@@ -1,12 +1,12 @@
 
 import numpy as np
 import time
-import matplotlib.pyplot as plt
 import keras.models as km
 from keras.datasets import mnist
 from keras.models import Sequential
 from keras.layers.core import Dense, Flatten, Dropout, Activation
 from keras.utils import np_utils
+import matplotlib.pyplot as plt
 
 predictions = ['T-shirt/top', 'trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
diff --git a/mnist.h5 b/mnist.h5
index 731ca1a..a853c00 100644
Binary files a/mnist.h5 and b/mnist.h5 differ
diff --git a/mnist.json b/mnist.json
index 0c9688a..d301995 100644
--- a/mnist.json
+++ b/mnist.json
@@ -1 +1 @@
-{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "batch_input_shape": [null, 784], "dtype": "float32", "units": 512, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_1", "trainable": true, "activation": "relu"}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "rate": 0.2, "noise_shape": null, "seed": null}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 512, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_2", "trainable": true, "activation": "relu"}}, {"class_name": "Dropout", "config": {"name": "dropout_2", "trainable": true, "rate": 0.2, "noise_shape": null, "seed": null}}, {"class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "units": 10, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_3", "trainable": true, "activation": "softmax"}}]}, "keras_version": "2.2.4", "backend": "tensorflow"}
\ No newline at end of file
+{"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 784], "dtype": "float32", "sparse": false, "ragged": false, "name": "dense_input"}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "batch_input_shape": [null, 784], "dtype": "float32", "units": 512, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation", "trainable": true, "dtype": "float32", "activation": "relu"}}, {"class_name": "Dropout", "config": {"name": "dropout", "trainable": true, "dtype": "float32", "rate": 0.2, "noise_shape": null, "seed": null}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 512, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_1", "trainable": true, "dtype": "float32", "activation": "relu"}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": "float32", "rate": 0.2, "noise_shape": null, "seed": null}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 10, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Activation", "config": {"name": "activation_2", "trainable": true, "dtype": "float32", "activation": "softmax"}}]}, "keras_version": "2.4.0", "backend": "tensorflow"}
\ No newline at end of file
diff --git a/mnist_keras.py b/mnist_keras.py
index cb7beb5..71e3b0c 100755
--- a/mnist_keras.py
+++ b/mnist_keras.py
@@ -1,11 +1,11 @@
 
 import numpy as np
 import time
-import matplotlib.pyplot as plt
 from keras.datasets import mnist
 from keras.models import Sequential
 from keras.layers.core import Dense, Flatten, Dropout, Activation
 from keras.utils import np_utils
+import matplotlib.pyplot as plt
 
 (X_train, y_train), (X_test, y_test) = mnist.load_data()
 num_pixels = X_train.shape[1] * X_train.shape[2]
diff --git a/save_model_mnist_keras.py b/save_model_mnist_keras.py
index ce0cd49..a8bd387 100644
--- a/save_model_mnist_keras.py
+++ b/save_model_mnist_keras.py
@@ -1,11 +1,11 @@
 
 import numpy as np
 import time
-import matplotlib.pyplot as plt
 from keras.datasets import mnist
 from keras.models import Sequential
 from keras.layers.core import Dense, Flatten, Dropout, Activation
 from keras.utils import np_utils
+import matplotlib.pyplot as plt
 
 predictions = ['T-shirt/top', 'trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']