Create Experiment_CIFAR10.py

Experiment_CIFAR10.py

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+
+
+import numpy as np
+
+import keras.activations
+from keras import backend as K
+from keras.engine.topology import Layer
+
+from keras import activations
+from keras import initializers
+from keras import regularizers
+from keras import constraints
+
+from keras.datasets import cifar10, cifar100
+from keras.utils import to_categorical
+
+(x_train, y_train), (x_test, y_test) = cifar10.load_data()
+# (x_train, y_train), (x_test, y_test) = cifar100.load_data()
+
+y_train = to_categorical(y_train)
+y_test = to_categorical(y_test)
+
+from keras.layers import Input, Dense, Dropout, BatchNormalization
+from keras.models import Model
+from keras.optimizers import *
+
+from TTLayer import *
+
+tt_input_shape=[8, 8, 8, 6]
+tt_output_shape=[10, 10, 10, 10]
+tt_ranks=[1, 2, 2, 2, 1]
+
+input = Input((32, 32, 3))
+ttl = TT_Layer(tt_input_shape=tt_input_shape, tt_output_shape=tt_output_shape,
+               tt_ranks=tt_ranks,
+               activation='relu', use_bias=True,
+               kernel_regularizer=regularizers.l2(.001), )
+z1 = ttl(input)
+z1 = Dropout(.5)(z1)
+output = Dense(10, activation='softmax', kernel_regularizer=regularizers.l2(.01),)(z1)
+
+model = Model(input, output)
+
+model.compile(optimizer=Adam(1e-4), loss='categorical_crossentropy', metrics=['accuracy'])
+
+model.fit(x_train, y_train, batch_size=128, epochs=1000, validation_data=[x_test, y_test], verbose=2)
+