Model 1

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    print("Epochs: ", epochs)

    print("Batch_size: ", batch_size)

    print("Training loss: ", model.loss(x_train_reshaped, y_train))

    print("Test loss: ", model.loss(x_test_reshaped, y_test))

    print("Accuracy: ", model.accuracy(x_train_reshaped, y_train))

    print("Time spent: ", CumulativeTime(time_steps, n_timesteps))

    print("准确率:", model. Accuracy(x_train_reshaped, y_train))

    Epochs: 50000 Batch_size: 4000 Training loss: 0.8428 Test loss: 0.9553 Accuracy: 98.47% Time spent: 6.

    import numpy as np
    from numpy import mean
    from numpy import std
    from numpy import dstack
    from pandas import read_csv
    from matplotlib import pyplot as plt
    from keras.models import Sequential
    from keras.layers import Dense
    from keras.layers import Flatten
    from keras.layers import Dropout
    from keras.layers.convolutional import Conv1D
    from keras.layers.convolutional import MaxPooling1D
    from keras.utils import to_categorical
    
    
    def readucr(filename):
      data = np.loadtxt(filename, delimiter=",")
      y = data[:, 0]
      x = data[:, 1:]
      return x, y.astype(int)
    
    
    x_train, y_train = readucr("EDXRF_HC_TRAINING.csv")
    x_test, y_test = readucr("EDXRF_HC_TESTING.csv")
    
    
    
    
    #Converting kernals
    sample_size = x_train.shape[0] # Nn of samples in the train set
    time_steps = x_train.shape[1]# Number of features in the train set
    input_dimension = 1 # Each number represented by 1
    
    x_train_reshaped = x_train.reshape(sample_size, time_steps, input_dimension)
    print("After reshape train data set shape:\n",x_train_reshaped.shape)
    print("1 sample shape:\n",x_train_reshaped[0].shape)
    print ("an example sample:\n", x_train_reshaped[0])
    
    
    
    
    sample_size = x_test.shape[0] # Nn of samples in the train set # x_train is the input name
    time_steps = x_test.shape[1]# Number of features in the train set
    input_dimension = 1 # Each number represented by 1
    
    x_test_reshaped = x_test.reshape(sample_size, time_steps, input_dimension)
    print("After reshape train data set shape:\n",x_test_reshaped.shape)
    print("1 sample shape:\n",x_test_reshaped[0].shape)
    print ("an example sample:\n", x_test_reshaped[0])
    
    
    
    
    # def model
    model = Sequential()
    model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(1023, 1)))
    model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
    model.add(Dropout(0.5))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Flatten())
    model.add(Dense(100, activation='relu'))
    model.add(Dense(1, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    
    
    
    
    # fit and evaluate a model
    def evaluate_model(x_train_reshaped, y_train, x_test_reshaped, y_test):
    
      verbose, epochs, batch_size = 0, 500, 4000
      n_timesteps, n_features, n_outputs = x_train_reshaped.shape[1], x_train_reshaped.shape[2], y_train.shape[1]
      model = Sequential()
      model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))
      model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
      model.add(Dropout(0.5))
      model.add(MaxPooling1D(pool_size=2))
      model.add(Flatten())
      model.add(Dense(100, activation='relu'))
      model.add(Dense(n_outputs, activation='softmax'))
      model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
      # fit network
    
      # evaluate model
    history = model.fit(x_train_reshaped, y_train, epochs=500, validation_split=0.2, verbose=1 )
      
    model.summary()
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