Added my programs

Author: edwardyang12

SP20/CV/Bigger CNN FASHION.png

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+import numpy as np
+import matplotlib.pyplot as plt
+from tensorflow.keras import datasets, models, layers, losses
+
+# load data and preprocess it
+# (train_images, train_labels), (test_images, test_labels) = datasets.fashion_mnist.load_data()
+(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()
+train_images, test_images = train_images / 255.0, test_images / 255.0
+
+train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)
+test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)
+
+# design of actual NN
+model = models.Sequential()
+input_shape = (28, 28, 1)
+model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
+model.add(layers.MaxPooling2D((2, 2)))
+
+model.add(layers.Flatten())
+model.add(layers.Dense(32, activation='relu'))
+model.add(layers.Dense(10, activation='softmax'))
+
+# train NN
+model.compile(optimizer='adam',
+              loss=losses.sparse_categorical_crossentropy,
+              metrics=['accuracy'])
+
+# graph the learning process
+history = model.fit(train_images, train_labels, epochs=30,
+                    validation_data=(test_images, test_labels))
+plt.plot(history.history['accuracy'], label='accuracy')
+plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
+plt.xlabel('Epoch')
+plt.ylabel('Accuracy')
+plt.ylim([0.5, 1])
+plt.legend(loc='lower right')
+
+test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
+
+plt.show()

SP20/CV/Bigger CNN MNIST.png

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+import numpy as np
+import matplotlib.pyplot as plt
+from tensorflow.keras import datasets, models, layers, losses
+
+# load data and preprocess it
+#(train_images, train_labels), (test_images, test_labels) = datasets.fashion_mnist.load_data()
+(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()
+
+train_images, test_images = train_images / 255.0, test_images / 255.0
+
+train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)
+test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)
+
+# design of actual NN
+model = models.Sequential()
+input_shape = (28, 28, 1)
+model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
+
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Dropout(0.25))
+
+model.add(layers.Conv2D(32, (3, 3), activation='relu'))
+
+model.add(layers.Conv2D(16, (3, 3), activation='relu'))
+model.add(layers.MaxPooling2D((2, 2)))
+
+model.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(10, activation='softmax'))
+
+# train NN
+model.compile(optimizer='adam',
+              loss=losses.sparse_categorical_crossentropy,
+              metrics=['accuracy'])
+
+# graph the learning process
+history = model.fit(train_images, train_labels, epochs=30,
+                    validation_data=(test_images, test_labels))
+
+plt.plot(history.history['accuracy'], label='accuracy')
+plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
+plt.xlabel('Epoch')
+plt.ylabel('Accuracy')
+plt.ylim([0.5, 1])
+plt.legend(loc='lower right')
+
+test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
+
+plt.show()
+

SP20/CV/CNN FASHION.png

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+import numpy as np
+import matplotlib.pyplot as plt
+import tensorflow as tf
+from tensorflow.keras import datasets, models, layers, losses, applications
+
+# load data and preprocess it
+(train_images, train_labels), (test_images, test_labels) = datasets.fashion_mnist.load_data()
+
+train_images, test_images = train_images / 255.0, test_images / 255.0
+
+# resize image to 32 x 32 using bicubic interpolation
+train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)
+train_images = tf.image.resize(train_images,[32,32], method='bicubic')
+train_images = tf.image.grayscale_to_rgb(train_images)
+plt.imshow(train_images[1])
+
+test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)
+test_images = tf.image.resize(test_images,[32,32], method='bicubic')
+test_images = tf.image.grayscale_to_rgb(test_images)
+
+# load MobileNetV2
+base_model = applications.MobileNetV2(input_shape=(32, 32, 3), include_top=False, weights='imagenet')
+base_model.trainable = False
+
+# design of actual NN
+model = models.Sequential([base_model])
+
+model.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(10, activation='softmax'))
+
+# train NN
+model.compile(optimizer='adam',
+              loss=losses.sparse_categorical_crossentropy,
+              metrics=['accuracy'])
+
+# graph the learning process
+history = model.fit(train_images, train_labels, epochs=30,
+                    validation_data=(test_images, test_labels))
+
+plt.plot(history.history['accuracy'], label='accuracy')
+plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
+plt.xlabel('Epoch')
+plt.ylabel('Accuracy')
+plt.ylim([0.5, 1])
+plt.legend(loc='lower right')
+
+test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
+
+plt.show()
+
+