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Author: SergiosKar

AutoEncoder.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from tensorflow.contrib.keras.python.keras.callbacks import TensorBoard
+from tensorflow.contrib.keras.python.keras.datasets import mnist
+from tensorflow.contrib.keras.python.keras.engine import Input, Model
+from tensorflow.contrib.keras.python.keras.layers import Dense, Conv2D, MaxPooling2D, UpSampling2D
+from tensorflow.contrib.keras.python.keras import backend as K
+
+
+(x_train, _), (x_test, _) = mnist.load_data()
+
+if K.image_data_format() == 'channels_last':
+    shape_ord = (28, 28, 1)
+else:
+    shape_ord = (1, 28, 28)
+
+(x_train, _), (x_test, _) = mnist.load_data()
+
+x_train = x_train.astype('float32') / 255.
+x_test = x_test.astype('float32') / 255.
+
+x_train = np.reshape(x_train, ((x_train.shape[0],) + shape_ord))
+x_test = np.reshape(x_test, ((x_test.shape[0],) + shape_ord))
+
+
+
+
+input_img = Input(shape=(28, 28, 1))
+
+x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
+x = MaxPooling2D((2, 2), padding='same')(x)
+x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
+x = MaxPooling2D((2, 2), padding='same')(x)
+x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
+encoded = MaxPooling2D((2, 2), padding='same')(x)
+
+# at this point the representation is (4, 4, 8) i.e. 128-dimensional
+
+x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
+x = UpSampling2D((2, 2))(x)
+x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
+x = UpSampling2D((2, 2))(x)
+x = Conv2D(16, (3, 3), activation='relu')(x)
+x = UpSampling2D((2, 2))(x)
+decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
+
+conv_autoencoder = Model(input_img, decoded)
+conv_autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
+
+
+batch_size=128
+steps_per_epoch = np.int(np.floor(x_train.shape[0] / batch_size))
+conv_autoencoder.fit(x_train, x_train, epochs=50, batch_size=128,
+                     shuffle=True, validation_data=(x_test, x_test),
+                     callbacks=[TensorBoard(log_dir='./tf_autoencoder_logs')])
+
+
+decoded_imgs = conv_autoencoder.predict(x_test)
+
+n = 10
+plt.figure(figsize=(20, 4))
+for i in range(n):
+    # display original
+    ax = plt.subplot(2, n, i+1)
+    plt.imshow(x_test[i].reshape(28, 28))
+    plt.gray()
+    ax.get_xaxis().set_visible(False)
+    ax.get_yaxis().set_visible(False)
+
+    # display reconstruction
+    ax = plt.subplot(2, n, i + n + 1)
+    plt.imshow(decoded_imgs[i].reshape(28, 28))
+    plt.gray()
+    ax.get_xaxis().set_visible(False)
+    ax.get_yaxis().set_visible(False)
+plt.show()

CNN.py

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+
+from tensorflow.contrib.keras.python.keras.datasets import mnist
+from tensorflow.contrib.keras.python.keras.layers import Convolution2D, MaxPooling2D, Dropout, Flatten, Dense
+from tensorflow.contrib.keras.python.keras.models import Sequential
+from tensorflow.contrib.keras.python.keras.utils import np_utils
+
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+# Preprocess
+X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
+X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+X_train /= 255
+X_test /= 255
+
+# Preprocess  labels
+Y_train = np_utils.to_categorical(y_train, 10)
+Y_test = np_utils.to_categorical(y_test, 10)
+
+# Model
+model = Sequential()
+
+#defaultformat: (samples, rows, col
+model.add(Convolution2D(32, kernel_size=(3, 3),strides=(1, 1),activation='relu', input_shape=(28, 28, 1)))
+model.add(Convolution2D(32, kernel_size=(3, 3),strides=(1, 1), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Flatten())
+model.add(Dense(128, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(10, activation='softmax'))
+model.summary()
+
+
+model.compile(loss='categorical_crossentropy',
+              optimizer='adam',
+              metrics=['accuracy']
+              )
+
+
+model.fit(X_train, Y_train,
+          batch_size=32, epochs=10, verbose=1)
+
+
+score = model.evaluate(X_test, Y_test, verbose=0)
+print('Test loss:', score[0])
+print('Test accuracy:', score[1])

RNN.py

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+from tensorflow.contrib.keras.python.keras.datasets import imdb
+from tensorflow.contrib.keras.python.keras.layers import Embedding, SimpleRNN, Dropout, Dense, Activation, LSTM, GRU
+from tensorflow.contrib.keras.python.keras.models import Sequential
+from tensorflow.contrib.keras.python.keras.preprocessing import sequence
+
+max_features = 20000
+maxlen = 100
+batch_size = 32
+
+
+(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=max_features)
+
+X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
+X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
+print('X_train shape:', X_train.shape)
+print('X_test shape:', X_test.shape)
+
+
+model = Sequential()
+model.add(Embedding(max_features, 128, input_length=maxlen))
+
+#model.add(SimpleRNN(128))
+#model.add(GRU(128))
+model.add(LSTM(128))
+
+model.add(Dropout(0.5))
+model.add(Dense(1))
+model.add(Activation('sigmoid'))
+
+
+model.compile(loss='binary_crossentropy', optimizer='adam')
+
+
+model.fit(X_train, y_train, batch_size=batch_size, epochs=1,
+          validation_data=(X_test, y_test))