def train_model_cifar10( single_model, is_verify = False): ############################################################################ # FUNCTION DESCRIPTION: main function to train CIFAR-10 dataset ############################################################################ file = MAIN_FILE is_complete_model = check_complete_model(single_model) if not is_complete_model: single_model = get_latest_model_list(single_model,file) model_name = single_model[0] cur_model_num = get_current_model_number(model_name) model_index = get_new_model_number(cur_model_num) print("single_model: ",single_model) (x_train, y_train), (x_test, y_test) = load_data_cifar10() y_train, y_test = convert_class_vec2matrix(y_train,y_test) model = Sequential() tmp_single_model = get_topology_only(single_model) num_layer = count_model_layer(tmp_single_model) model = cnn_model_fn(model,num_layer,single_model) # initiate RMSprop optimizer opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6) # Let's train the model using RMSprop model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) x_train, x_test = format_data(x_train,x_test) if not DATA_AUGMENTATION: no_data_augmentation(model,x_train,x_test,y_train,y_test) else: data_augmentation(model,x_train,x_test,y_train,y_test) if not is_verify: save_model_keras_cifar10(model,single_model) # Score trained model. scores = model.evaluate(x_test, y_test, verbose=1) print('Test loss:', scores[0]) print('Test accuracy:', scores[1]) loss = scores[0] accuracy = scores[1] print("Model ", single_model) if not is_verify: save_trained_model_in_csv(file,single_model,scores) print('\n') clear_session() return accuracy
def train_model_svhn(single_model): file = MAIN_FILE_SVHN is_complete_model = check_complete_model(single_model) if not is_complete_model: single_model = get_latest_model_list(single_model, file) print("single_model: ", single_model) # (x_train, y_train), (x_test, y_test) = load_data_cifar10() # y_train, y_test = convert_class_vec2matrix(y_train,y_test) x_train, y_train, x_test, y_test = load_data_svhn() model = Sequential() tmp_single_model = get_topology_only(single_model) num_layer = count_model_layer(tmp_single_model) model = cnn_model_fn(model, num_layer, single_model) # initiate RMSprop optimizer opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6) # Let's train the model using RMSprop model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) # x_train, x_test = format_data(x_train,x_test) if not data_augmentation: no_data_augmentation(model, x_train, x_test, y_train, y_test) else: data_augmentation(model, x_train, x_test, y_train, y_test) save_model_keras_svhn(model, single_model) # Score trained model. scores = model.evaluate(x_test, y_test, verbose=1) print('Test loss:', scores[0]) print('Test accuracy:', scores[1]) loss = scores[0] accuracy = scores[1] print("Model ", single_model[:-2]) file_name = MAIN_FILE_SVHN save_trained_model_in_csv(file_name, single_model, scores) print('\n') clear_session() return accuracy
def fix_dict(dict_data): final_array = [] data = convert_dict_to_list(dict_data) for array in data: temp_array = array[:] array = get_topology_only(array) count = 0 for single_element in array: if single_element != LAYER_EMPTY: count += 1 if count < MAX_NUM_LAYER and array[count - 1] != LAYER_SOFTMAX: array[count] = LAYER_SOFTMAX temp_array[count + 1] = LAYER_SOFTMAX final_array.append(temp_array) return final_array
def cnn_model_fn_2(features, labels, mode): ############################################################################ # FUNCTION DESCRIPTION: main function to attach all topologies together into a model. ############################################################################ tmp_single_model = get_topology_only(GLOBAL_DATA) num_layer = count_model_layer(tmp_single_model) input_layer = tf.reshape(features["x"], [-1, 28, 28, 1]) layer = input_layer temp_layer = 0 for index in range(1, num_layer): if GLOBAL_DATA[index] == 'c_1': temp_layer = make_conv2d(layer, c_1) elif GLOBAL_DATA[index] == 'c_2': temp_layer = make_conv2d(layer, c_2) elif GLOBAL_DATA[index] == 'c_3': temp_layer = make_conv2d(layer, c_3) elif GLOBAL_DATA[index] == 'c_4': temp_layer = make_conv2d(layer, c_4) elif GLOBAL_DATA[index] == 'c_5': temp_layer = make_conv2d(layer, c_5) elif GLOBAL_DATA[index] == 'c_6': temp_layer = make_conv2d(layer, c_6) elif GLOBAL_DATA[index] == 'c_7': temp_layer = make_conv2d(layer, c_7) elif GLOBAL_DATA[index] == 'c_8': temp_layer = make_conv2d(layer, c_8) elif GLOBAL_DATA[index] == 'c_9': temp_layer = make_conv2d(layer, c_9) elif GLOBAL_DATA[index] == 'c_10': temp_layer = make_conv2d(layer, c_10) elif GLOBAL_DATA[index] == 'c_11': temp_layer = make_conv2d(layer, c_11) elif GLOBAL_DATA[index] == 'c_12': temp_layer = make_conv2d(layer, c_12) elif GLOBAL_DATA[index] == 'm_1': temp_layer = make_pool2d(layer, m_1) elif GLOBAL_DATA[index] == 'm_2': temp_layer = make_pool2d(layer, m_2) elif GLOBAL_DATA[index] == 'm_3': temp_layer = make_pool2d(layer, m_3) elif GLOBAL_DATA[index] == 's': break layer = temp_layer shape_array = layer.get_shape() pool2_flat = tf.reshape( layer, [-1, shape_array[1] * shape_array[2] * shape_array[3]]) dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu) dropout = tf.layers.dropout(inputs=dense, rate=0.5, training=mode == tf.estimator.ModeKeys.TRAIN) logits = tf.layers.dense(inputs=dropout, units=10) predictions = { "classes": tf.argmax(input=logits, axis=1), "probabilities": tf.nn.softmax(logits, name="softmax_tensor") } if mode == tf.estimator.ModeKeys.PREDICT: return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions) loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) if mode == tf.estimator.ModeKeys.TRAIN: optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001) train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step()) return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op) eval_metric_ops = { "accuracy": tf.metrics.accuracy(labels=labels, predictions=predictions["classes"]) } return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)