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some test
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def Create_CNN_By_Yourself():
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filename = input() + ".py"
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print("file name", filename)
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with open(filename, "w", encoding="utf-8") as f:
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f.write(
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"import datetime\nimport os\nimport numpy as np\nimport tensorflow as tf\nfrom utils_seventyOne_of_Filters \nimport loadData, plot_history_tf, plot_heat_map\n")
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f.write('Project_PATH = "../')
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print("\n\n\n" + "Enter the path to save data from the start of the secondary folder,Used / arnt\\")
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Project_PATH = input()
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f.write(Project_PATH + "\\" + '"\n')
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print('Project_PATH=' + Project_PATH)
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f.write(
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'log_dir = Project_PATH + "logs/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")\nmodel_Path_one = Project_PATH + "model/\n')
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print("\n\n\n" + "Enter model name ")
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model_name = input()
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f.write(model_name + '.h5"\n')
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print('model_name=' + model_name)
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print("\n\n\n" + 'Enter RATIO (usually used 0.3)')
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RATIO = input()
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f.write("RETIO=" + RATIO + '\n')
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print('RATIO=' + RATIO)
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print("\n\n\n" + 'Enter RANDOM_SEED ')
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RANDOM_SEED = input()
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f.write('RANDOM_SEED=' + RANDOM_SEED + '\n')
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print('RANDOM_SEED=' + RANDOM_SEED)
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print('\n\n\n' + "Enter BATCH_SIZE,this number of samples per gradient update")
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BATCH_SIZE = input('')
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f.write("BATCH_SIZE=" + BATCH_SIZE + '\n')
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print('BATHC_SIZE=' + BATCH_SIZE)
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print('\n\n\n' + "Enter NUM_POCHS")
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NUM_POCHS = input()
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f.write('NUM_EPOCHS =' + NUM_POCHS + '\n')
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# def CNN mode
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print('Name of this CNN model')
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CNN_NAME = input()
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#Input array type
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f.write(
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'def ' + CNN_NAME + '():\n leavlOneModel = tf.keras.models.Sequential([\n tf.keras.layers.InputLayer(input_shape=(300,)),\n tf.keras.layers.Reshape(target_shape=(300, 1)),\n')
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# def CNN model
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print('\n\n\nEnter your CNN model layer.')
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CNN_Layer = input()
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CNN_Layer_Int = int(CNN_Layer)
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j = 1
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while (j <=CNN_Layer_Int):
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print('\n\n\nEnter the parameters for the ' + j + ' layer of your CNN model.')
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# this is Conv1D code, you can change by your self
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print('Enter your Conv1D filters , kernel_size,strides,padding,sctivation. And Enter once for each parameter')
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filters = int(input())
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kernel_size = int(input())
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strides = int(input())
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padding = input()
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activation = input()
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f.write(
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" tf.keras.layers.Conv1D(filters=" + filters + ", kernel_size=" + kernel_size + ", strides=" + strides + ", padding='" + padding + "', activation='" + activation + "),\n")
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# Pool paremeters
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print('\n\n\nNow,you shoud enter your Pool Paremeters')
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# this is pool1D,you can change to your self
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##because of my CNN used only MaxPool1D,I commented out this line of code,you can uncommented and use this code to change the type for your PoolFunction
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# print('\n\n\nEnter your Pool types,such as MaxPool ')
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# Pool_types=input()
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# f.write('tf.keras.layers.'+Pool_types+'1D')
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# If you use the above paragraph code, you need commented this paragraph
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f.write(' tf.keras.layers.MaxPool1D')
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print('\n\n\nEnter the filters strides padding by Enter once for each parameter')
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pool_size = int(input())
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strides_Pool = int(input())
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padding_Pool = int(input())
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f.write("(pool_size=" + pool_size + ", strides=" + strides_Pool + ", padding='" + padding_Pool + "'),\n")
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j = j + 1
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# Now we create the fully connected layer
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# def Dense
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print('\n\n\nEnter your Dense layer')
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k = 1
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Dense_Layer = int(input())
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while (k <= Dense_Layer):
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print('\n\n\nEnter the type of calculation layer,such as Flatten Dropout')
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calculation = input()
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print('In this calculation, you needed parameters. Such as Dropout, you can enter 0.3 or otherthing,but you should add the complete parameter setup (including instructions).Like rate=0.2 ,this is Dropout parameter needs from')
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calculation_parameters = input()
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f.write(" tf.keras.layers."+calculation+"("+calculation_parameters+"),")
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#Now,lets make the Dence
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print('Now.;lets take the Dence'
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'you need take how much of your categories and whitch calculation you need.such as Relu,softmax,tanH')
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categories_Number=input()
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activation_Dense=input()
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f.write(" tf.keras.layers.Dense("+categories_Number+", activation='"+activation_Dense+"'),\n" )
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k=k+1
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f.write(" ])\n return leavlOneModel\n")
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# def main
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f.write(
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"def main():\n X_train, X_test, y_train, y_test = loadData(RATIO, RANDOM_SEED)\n if os.path.exists(model_Path_one):\n print(' get model in h5 file')\n model = tf.keras.models.load_model(filepath=model_Path_one)\n else:\n # create new model(if model unexists)\n model = CNN_model_level_one()\n model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n model.summary()\n # TB make\n tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir,\n histogram_freq=1,\n )\n # Training and validation\n history = model.fit(X_train, y_train, epochs=NUM_EPOCHS,\n batch_size=BATCH_SIZE,\n validation_data=(X_test, y_test),\n callbacks=[tensorboard_callback])\n # validation_split=RATIO,\n model.save(filepath=model_Path_one)\n plot_history_tf(history)\n y_pred = np.argmax(model.predict(X_test), axis=-1)\n plot_heat_map(y_test, y_pred)")
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# print(str=f.read(filename+".py"))
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f.write("if __name__ == '__main__':\n main()")
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if __name__ == '__main__':
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Create_CNN_By_Yourself()
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