Added files. /JL

load_model_mnist_keras.py

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+
+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
+
+predictions = ['T-shirt/top', 'trouser', 'Pullover', 'Dress', 'Coat',
+              'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
+
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+num_pixels = X_train.shape[1] * X_train.shape[2]
+num_classes = 10
+
+t1 = time.time()
+X_train = X_train.reshape(60000, 784) / 255
+X_test = X_test.reshape(10000, 784) / 255
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+
+# let's print the shape before we reshape and normalize
+print("X_train shape", X_train.shape)
+print("y_train shape", y_train.shape)
+print("X_test shape", X_test.shape)
+print("y_test shape", y_test.shape)
+
+Y_train = np_utils.to_categorical(y_train, num_classes)
+Y_test = np_utils.to_categorical(y_test, num_classes)
+t2 = time.time()
+print("Preprocessing took %.2f sec." % (t2 - t1))
+
+print('Loading model and weights.')
+json_file = open('mnist.json', 'r')
+loaded_nnet = json_file.read()
+json_file.close()
+
+model = km.model_from_json(loaded_nnet)
+model.load_weights('mnist.h5')
+
+print("Training network ...")
+model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+print("Making predictions...")
+t1 = time.time()
+for i in range(9):
+	img = np.array(X_test[i][np.newaxis,:])
+	preds = model.predict_classes(img)
+	print("Image[", i, "] - Me thinks me saw a : ", predictions[int(preds[0])] )
+t2 = time.time()
+print("Predictions took %.2f sec." % (t2 - t1))
+
+
+

mnist.json

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+{"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"}

mnist_keras.py

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+
+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
+
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+num_pixels = X_train.shape[1] * X_train.shape[2]
+num_classes = 10
+
+fig = plt.figure()
+for i in range(9):
+  plt.subplot(3,3,i+1)
+  plt.tight_layout()
+  plt.imshow(X_test[i], cmap='gray', interpolation='none')
+  plt.title("Digit: %d" % (y_test[i]))
+  plt.xticks([])
+  plt.yticks([])
+fig.show()
+t1 = time.time()
+
+X_train = X_train.reshape(60000, 784) / 255
+X_test = X_test.reshape(10000, 784) / 255
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+
+# let's print the shape before we reshape and normalize
+print("X_train shape", X_train.shape)
+print("y_train shape", y_train.shape)
+print("X_test shape", X_test.shape)
+print("y_test shape", y_test.shape)
+
+Y_train = np_utils.to_categorical(y_train, num_classes)
+Y_test = np_utils.to_categorical(y_test, num_classes)
+t2 = time.time()
+print("Preprocessing took %.2f sec." % (t2 - t1))
+
+t1 = time.time()
+# building a linear stack of layers with the sequential model
+model = Sequential()
+model.add(Dense(512, input_shape = (784,)))
+model.add(Activation('relu'))                            
+model.add(Dropout(0.2))
+model.add(Dense(512))
+model.add(Activation('relu'))
+model.add(Dropout(0.2))
+model.add(Dense(10))
+model.add(Activation('softmax'))
+
+
+print("Training network ...")
+model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+print("Fitting data")
+model.fit(X_train, Y_train, batch_size=128, epochs=15, 
+          verbose=1, validation_data=(X_test, Y_test))
+t2 = time.time()
+print("Training took %.2f sec." % (t2 - t1))
+
+print("Making predictions...")
+t1 = time.time()
+for i in range(9):
+	img = np.array(X_test[i][np.newaxis,:])
+	preds = model.predict_classes(img)
+	print("Image[", i, "] - Me thinks me saw a : ", int(preds[0]) )
+t2 = time.time()
+print("Predictions took %.2f sec." % (t2 - t1))
+
+
+

save_model_mnist_keras.py

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+
+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
+
+predictions = ['T-shirt/top', 'trouser', 'Pullover', 'Dress', 'Coat',
+              'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
+
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+num_pixels = X_train.shape[1] * X_train.shape[2]
+num_classes = 10
+
+fig = plt.figure()
+for i in range(9):
+  plt.subplot(3,3,i+1)
+  plt.tight_layout()
+  plt.imshow(X_test[i], cmap='gray', interpolation='none')
+  plt.title("Digit: %d" % (y_test[i]))
+  plt.xticks([])
+  plt.yticks([])
+fig.show()
+t1 = time.time()
+
+X_train = X_train.reshape(60000, 784) / 255
+X_test = X_test.reshape(10000, 784) / 255
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+
+# let's print the shape before we reshape and normalize
+print("X_train shape", X_train.shape)
+print("y_train shape", y_train.shape)
+print("X_test shape", X_test.shape)
+print("y_test shape", y_test.shape)
+
+Y_train = np_utils.to_categorical(y_train, num_classes)
+Y_test = np_utils.to_categorical(y_test, num_classes)
+t2 = time.time()
+print("Preprocessing took %.2f sec." % (t2 - t1))
+
+t1 = time.time()
+# building a linear stack of layers with the sequential model
+model = Sequential()
+model.add(Dense(512, input_shape = (784,)))
+model.add(Activation('relu'))                            
+model.add(Dropout(0.2))
+model.add(Dense(512))
+model.add(Activation('relu'))
+model.add(Dropout(0.2))
+model.add(Dense(10))
+model.add(Activation('softmax'))
+
+
+print("Training network ...")
+model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+print("Fitting data")
+model.fit(X_train, Y_train, batch_size=128, epochs=15, 
+          verbose=1, validation_data=(X_test, Y_test))
+t2 = time.time()
+print("Training took %.2f sec." % (t2 - t1))
+
+print('Saving model and weights.')
+model_json = model.to_json()
+with open("mnist.json", "w") as json_file:
+    json_file.write(model_json)
+model.save_weights('mnist.h5')
+
+print("Making predictions...")
+t1 = time.time()
+for i in range(9):
+	img = np.array(X_test[i][np.newaxis,:])
+	preds = model.predict_classes(img)
+	print("Image[", i, "] - Me thinks me saw a : ", predictions[int(preds[0])] )
+t2 = time.time()
+print("Predictions took %.2f sec." % (t2 - t1))
+
+
+