因此，我试图学习机器学习的工作原理，然后从一个简单的股价预测程序开始。我已经尝试了几天来缩小问题范围，但是我的研究和进展却停滞不前。我的问题是准确度不会增加，val_accuracy也不会更改。我将数据集缩小为一个较小的集，以观察行为，当然，仍然没有改变...
我尝试切换丢失，激活状态，并尝试了一些更改来准备数据...我不知道发生了什么事？这仅在一个股票行情收录器上...（试图使用一种模型进行前100个价格）
我的图层/模型
def createModel(X_train):
'''
@description -
'''
# Model
model = Sequential()
model.add(LSTM(512, activation = 'relu', return_sequences = True, input_shape = (X_train.shape[1:])))
model.add(Dropout(0.3))
model.add(LSTM(512, activation = 'relu', return_sequences = True))
model.add(Dropout(0.3))
model.add(LSTM(256, activation = 'relu', return_sequences = True))
model.add(Dropout(0.3))
model.add(LSTM(128, activation = 'relu', return_sequences = False))
model.add(Dropout(0.2))
model.add(Dense(1, activation = 'sigmoid'))
# print(model.summary())
# opt = tf.keras.optimizers.Adam(learning_rate = 0.01)
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
return model
我有以下代码段可读取数据并准备数据。
if filename.endswith('.csv'):
data = pd.read_csv(filename)
# Clean up file name to extract ticker
filename = filename.replace('.csv', '')
data = data.drop(['Dividends', 'Stock Splits'], axis = 1)
data['Date'] = list(map(convertDateToNumber, data['Date']))
data.set_index('Date', inplace = True)
# Shift for a new column to do calculations on, then drop the shifted column after
data['Per Change'] = data['Open'].shift(1)
data['Percent Change'] = list(map(calculatePercentChange, data['Open'], data['Close']))
data['Class'] = (list(map(classify, data['Open'], data['Close'])))
# Drop the unnecessary headers now...
data = data.drop('Per Change', 1)
data.fillna(method = "ffill", inplace = True)
data.dropna(inplace = True)
trainingData = int(len(data) * 0.75)
training_data = data.head(trainingData).values.tolist()
training_data = scaler.fit_transform(training_data)
testingData = int(len(data) * 0.25)
testing_data = data.tail(testingData).values.tolist()
testing_data = scaler.fit_transform(testing_data)
X_train = []
y_train = []
for i in range(training_data.shape[0]):
X_train.append(training_data[i])
y_train.append(training_data[i, 2])
X_train, y_train = np.array(X_train), np.array(y_train)
X_train = X_train.reshape(X_train.shape[0], 1, X_train.shape[1])
# y_train = y_train.reshape(y_train.shape[0], 1)
# Test Data
X_test = []
y_test = []
for i in range(testing_data.shape[0]):
X_test.append(testing_data[i])
y_test.append(testing_data[i, 2])
X_test, y_test = np.array(X_test), np.array(y_test)
X_test = X_test.reshape(X_test.shape[0], 1, X_test.shape[1])
# y_test = y_test.reshape(y_test.shape[0], 1)
# Create the model
model = createModel(X_train)
# Evaluate the model
print('')
loss, acc = model.evaluate(X_test, y_test)
print("\n---------- Untrained model, accuracy: {:5.2f}% ----------\n".format(100 * acc))
if os.path.isdir(modelPath.replace('data model.h5', '')):
try:
model = tf.keras.models.load_model(modelPath, compile = True)
# Re-evaluate the model
loss, acc = model.evaluate(X_test, y_test)
print("\n---------- Restored model, accuracy: {:5.2f}% ----------\n".format(100 * acc))
except:
pass
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir = log_dir, histogram_freq = 1)
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath = checkpointPath, save_weights_only = True, monitor = 'accuracy', mode = 'max', save_freq = 5)
# To throw it all together... fit() trains the model
model.fit(X_train, y_train, validation_data = (X_test, y_test), shuffle = True, epochs = 50, batch_size = 500, callbacks = [tensorboard_callback, model_checkpoint_callback])
model.save(modelPath)
# # Call the model protocol
y_pred = model.predict(X_test)
scale = 1 / scaler.scale_[0]
y_test = y_test * scale
y_pred = y_pred * scale
plt.plot(y_test, color = 'blue', label = '{} Real Stock Price'.format(filename + ' ' + companyNameToTicker[filename]))
plt.plot(y_pred, color = 'red', label = '{} Predicted Stock Price'.format(filename + ' ' + companyNameToTicker[filename]))
plt.title('{} Stock Price Prediction'.format(filename + ' ' + companyNameToTicker[filename]))
plt.xlabel('Time??')
plt.ylabel('{} Stock Prediction'.format(filename + ' ' + companyNameToTicker[filename]))
plt.legend()
# plt.ion()
# plt.pause(0.05)
plt.show()
这是上面的代码产生的输出...
1/1 [==============================] - ETA: 0s - loss: 0.5488 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (10.187773). Check your callbacks.
1/1 [==============================] - 1s 822ms/step - loss: 0.5488 - accuracy: 0.0476 - val_loss: 0.4729 - val_accuracy: 0.1429
Epoch 3/50
1/1 [==============================] - 1s 517ms/step - loss: 0.5472 - accuracy: 0.0476 - val_loss: 0.4725 - val_accuracy: 0.1429
Epoch 4/50
1/1 [==============================] - 0s 485ms/step - loss: 0.5476 - accuracy: 0.0476 - val_loss: 0.4723 - val_accuracy: 0.1429
Epoch 5/50
1/1 [==============================] - ETA: 0s - loss: 0.5484 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.438490). Check your callbacks.
1/1 [==============================] - 1s 507ms/step - loss: 0.5484 - accuracy: 0.0476 - val_loss: 0.4725 - val_accuracy: 0.1429
Epoch 6/50
1/1 [==============================] - 1s 527ms/step - loss: 0.5476 - accuracy: 0.0476 - val_loss: 0.4732 - val_accuracy: 0.1429
Epoch 7/50
1/1 [==============================] - 0s 413ms/step - loss: 0.5481 - accuracy: 0.0476 - val_loss: 0.4738 - val_accuracy: 0.1429
Epoch 8/50
1/1 [==============================] - 0s 491ms/step - loss: 0.5475 - accuracy: 0.0476 - val_loss: 0.4743 - val_accuracy: 0.1429
Epoch 9/50
1/1 [==============================] - 0s 408ms/step - loss: 0.5479 - accuracy: 0.0476 - val_loss: 0.4748 - val_accuracy: 0.1429
Epoch 10/50
1/1 [==============================] - ETA: 0s - loss: 0.5478 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.434556). Check your callbacks.
1/1 [==============================] - 0s 482ms/step - loss: 0.5478 - accuracy: 0.0476 - val_loss: 0.4751 - val_accuracy: 0.1429
Epoch 11/50
1/1 [==============================] - 1s 535ms/step - loss: 0.5475 - accuracy: 0.0476 - val_loss: 0.4754 - val_accuracy: 0.1429
Epoch 12/50
1/1 [==============================] - 0s 408ms/step - loss: 0.5485 - accuracy: 0.0476 - val_loss: 0.4758 - val_accuracy: 0.1429
Epoch 13/50
1/1 [==============================] - 0s 392ms/step - loss: 0.5487 - accuracy: 0.0476 - val_loss: 0.4764 - val_accuracy: 0.1429
Epoch 14/50
1/1 [==============================] - 0s 460ms/step - loss: 0.5488 - accuracy: 0.0476 - val_loss: 0.4768 - val_accuracy: 0.1429
Epoch 15/50
1/1 [==============================] - ETA: 0s - loss: 0.5486 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.388608). Check your callbacks.
1/1 [==============================] - 0s 397ms/step - loss: 0.5486 - accuracy: 0.0476 - val_loss: 0.4770 - val_accuracy: 0.1429
Epoch 16/50
1/1 [==============================] - 1s 573ms/step - loss: 0.5475 - accuracy: 0.0476 - val_loss: 0.4770 - val_accuracy: 0.1429
Epoch 17/50
1/1 [==============================] - 0s 456ms/step - loss: 0.5479 - accuracy: 0.0476 - val_loss: 0.4766 - val_accuracy: 0.1429
Epoch 18/50
1/1 [==============================] - 0s 392ms/step - loss: 0.5476 - accuracy: 0.0476 - val_loss: 0.4763 - val_accuracy: 0.1429
Epoch 19/50
1/1 [==============================] - 0s 404ms/step - loss: 0.5479 - accuracy: 0.0476 - val_loss: 0.4760 - val_accuracy: 0.1429
Epoch 20/50
1/1 [==============================] - ETA: 0s - loss: 0.5479 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.362628). Check your callbacks.
1/1 [==============================] - ETA: 0s - loss: 0.5488 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (10.187773). Check your callbacks.
1/1 [==============================] - 1s 822ms/step - loss: 0.5488 - accuracy: 0.0476 - val_loss: 0.4729 - val_accuracy: 0.1429
Epoch 3/50
1/1 [==============================] - 1s 517ms/step - loss: 0.5472 - accuracy: 0.0476 - val_loss: 0.4725 - val_accuracy: 0.1429
Epoch 4/50
1/1 [==============================] - 0s 485ms/step - loss: 0.5476 - accuracy: 0.0476 - val_loss: 0.4723 - val_accuracy: 0.1429
Epoch 5/50
1/1 [==============================] - ETA: 0s - loss: 0.5484 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.438490). Check your callbacks.
1/1 [==============================] - 1s 507ms/step - loss: 0.5484 - accuracy: 0.0476 - val_loss: 0.4725 - val_accuracy: 0.1429
Epoch 6/50
1/1 [==============================] - 1s 527ms/step - loss: 0.5476 - accuracy: 0.0476 - val_loss: 0.4732 - val_accuracy: 0.1429
Epoch 7/50
1/1 [==============================] - 0s 413ms/step - loss: 0.5481 - accuracy: 0.0476 - val_loss: 0.4738 - val_accuracy: 0.1429
Epoch 8/50
1/1 [==============================] - 0s 491ms/step - loss: 0.5475 - accuracy: 0.0476 - val_loss: 0.4743 - val_accuracy: 0.1429
Epoch 9/50
1/1 [==============================] - 0s 408ms/step - loss: 0.5479 - accuracy: 0.0476 - val_loss: 0.4748 - val_accuracy: 0.1429
Epoch 10/50
1/1 [==============================] - ETA: 0s - loss: 0.5478 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.434556). Check your callbacks.
1/1 [==============================] - 0s 482ms/step - loss: 0.5478 - accuracy: 0.0476 - val_loss: 0.4751 - val_accuracy: 0.1429
Epoch 11/50
1/1 [==============================] - 1s 535ms/step - loss: 0.5475 - accuracy: 0.0476 - val_loss: 0.4754 - val_accuracy: 0.1429
Epoch 12/50
1/1 [==============================] - 0s 408ms/step - loss: 0.5485 - accuracy: 0.0476 - val_loss: 0.4758 - val_accuracy: 0.1429
Epoch 13/50
1/1 [==============================] - 0s 392ms/step - loss: 0.5487 - accuracy: 0.0476 - val_loss: 0.4764 - val_accuracy: 0.1429
Epoch 14/50
1/1 [==============================] - 0s 460ms/step - loss: 0.5488 - accuracy: 0.0476 - val_loss: 0.4768 - val_accuracy: 0.1429
Epoch 15/50
1/1 [==============================] - ETA: 0s - loss: 0.5486 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.388608). Check your callbacks.
1/1 [==============================] - 0s 397ms/step - loss: 0.5486 - accuracy: 0.0476 - val_loss: 0.4770 - val_accuracy: 0.1429
Epoch 16/50
1/1 [==============================] - 1s 573ms/step - loss: 0.5475 - accuracy: 0.0476 - val_loss: 0.4770 - val_accuracy: 0.1429
Epoch 17/50
1/1 [==============================] - 0s 456ms/step - loss: 0.5479 - accuracy: 0.0476 - val_loss: 0.4766 - val_accuracy: 0.1429
Epoch 18/50
1/1 [==============================] - 0s 392ms/step - loss: 0.5476 - accuracy: 0.0476 - val_loss: 0.4763 - val_accuracy: 0.1429
Epoch 19/50
1/1 [==============================] - 0s 404ms/step - loss: 0.5479 - accuracy: 0.0476 - val_loss: 0.4760 - val_accuracy: 0.1429
Epoch 20/50
1/1 [==============================] - ETA: 0s - loss: 0.5479 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.362628). Check your callbacks.
1/1 [==============================] - 0s 452ms/step - loss: 0.5479 - accuracy: 0.0476 - val_loss: 0.4758 - val_accuracy: 0.1429
Epoch 21/50
1/1 [==============================] - 0s 473ms/step - loss: 0.5476 - accuracy: 0.0476 - val_loss: 0.4753 - val_accuracy: 0.1429
Epoch 22/50
1/1 [==============================] - 0s 428ms/step - loss: 0.5496 - accuracy: 0.0476 - val_loss: 0.4744 - val_accuracy: 0.1429
Epoch 23/50
1/1 [==============================] - 1s 584ms/step - loss: 0.5475 - accuracy: 0.0476 - val_loss: 0.4741 - val_accuracy: 0.1429
Epoch 24/50
1/1 [==============================] - 0s 446ms/step - loss: 0.5478 - accuracy: 0.0476 - val_loss: 0.4743 - val_accuracy: 0.1429
Epoch 25/50
1/1 [==============================] - ETA: 0s - loss: 0.5476 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.530422). Check your callbacks.
1/1 [==============================] - 1s 646ms/step - loss: 0.5476 - accuracy: 0.0476 - val_loss: 0.4746 - val_accuracy: 0.1429
Epoch 26/50
1/1 [==============================] - 1s 506ms/step - loss: 0.5487 - accuracy: 0.0476 - val_loss: 0.4756 - val_accuracy: 0.1429
Epoch 27/50
1/1 [==============================] - 0s 413ms/step - loss: 0.5482 - accuracy: 0.0476 - val_loss: 0.4765 - val_accuracy: 0.1429
Epoch 28/50
1/1 [==============================] - 0s 382ms/step - loss: 0.5481 - accuracy: 0.0476 - val_loss: 0.4772 - val_accuracy: 0.1429
Epoch 29/50
1/1 [==============================] - 0s 421ms/step - loss: 0.5487 - accuracy: 0.0476 - val_loss: 0.4774 - val_accuracy: 0.1429
Epoch 30/50
1/1 [==============================] - ETA: 0s - loss: 0.5483 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.657228). Check your callbacks.
1/1 [==============================] - 1s 955ms/step - loss: 0.5483 - accuracy: 0.0476 - val_loss: 0.4782 - val_accuracy: 0.1429
Epoch 31/50
1/1 [==============================] - 1s 634ms/step - loss: 0.5475 - accuracy: 0.0476 - val_loss: 0.4792 - val_accuracy: 0.1429
Epoch 32/50
1/1 [==============================] - 0s 364ms/step - loss: 0.5479 - accuracy: 0.0476 - val_loss: 0.4800 - val_accuracy: 0.1429
Epoch 33/50
1/1 [==============================] - 0s 404ms/step - loss: 0.5478 - accuracy: 0.0476 - val_loss: 0.4808 - val_accuracy: 0.1429
Epoch 34/50
1/1 [==============================] - 0s 381ms/step - loss: 0.5477 - accuracy: 0.0476 - val_loss: 0.4812 - val_accuracy: 0.1429
Epoch 35/50
1/1 [==============================] - ETA: 0s - loss: 0.5476 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.342873). Check your callbacks.
1/1 [==============================] - 1s 524ms/step - loss: 0.5476 - accuracy: 0.0476 - val_loss: 0.4810 - val_accuracy: 0.1429
Epoch 36/50
1/1 [==============================] - 0s 442ms/step - loss: 0.5485 - accuracy: 0.0476 - val_loss: 0.4808 - val_accuracy: 0.1429
Epoch 37/50
1/1 [==============================] - 1s 514ms/step - loss: 0.5493 - accuracy: 0.0476 - val_loss: 0.4805 - val_accuracy: 0.1429
Epoch 38/50
1/1 [==============================] - 1s 630ms/step - loss: 0.5503 - accuracy: 0.0476 - val_loss: 0.4806 - val_accuracy: 0.1429
Epoch 39/50
1/1 [==============================] - ETA: 0s - loss: 0.5478 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.724169). Check your callbacks.
1/1 [==============================] - 1s 1s/step - loss: 0.5478 - accuracy: 0.0476 - val_loss: 0.4812 - val_accuracy: 0.1429
Epoch 40/50
1/1 [==============================] - ETA: 0s - loss: 0.5475 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.356633). Check your callbacks.
1/1 [==============================] - 0s 400ms/step - loss: 0.5475 - accuracy: 0.0476 - val_loss: 0.4813 - val_accuracy: 0.1429
Epoch 41/50
1/1 [==============================] - 1s 625ms/step - loss: 0.5479 - accuracy: 0.0476 - val_loss: 0.4814 - val_accuracy: 0.1429
Epoch 42/50
1/1 [==============================] - 1s 671ms/step - loss: 0.5481 - accuracy: 0.0476 - val_loss: 0.4810 - val_accuracy: 0.1429
Epoch 43/50
1/1 [==============================] - 1s 527ms/step - loss: 0.5482 - accuracy: 0.0476 - val_loss: 0.4803 - val_accuracy: 0.1429
Epoch 44/50
1/1 [==============================] - 1s 688ms/step - loss: 0.5479 - accuracy: 0.0476 - val_loss: 0.4797 - val_accuracy: 0.1429
Epoch 45/50
1/1 [==============================] - ETA: 0s - loss: 0.5475 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.479657). Check your callbacks.
1/1 [==============================] - 1s 505ms/step - loss: 0.5475 - accuracy: 0.0476 - val_loss: 0.4789 - val_accuracy: 0.1429
Epoch 46/50
1/1 [==============================] - 1s 637ms/step - loss: 0.5479 - accuracy: 0.0476 - val_loss: 0.4776 - val_accuracy: 0.1429
Epoch 47/50
1/1 [==============================] - 0s 383ms/step - loss: 0.5490 - accuracy: 0.0476 - val_loss: 0.4772 - val_accuracy: 0.1429
Epoch 48/50
1/1 [==============================] - 0s 420ms/step - loss: 0.5486 - accuracy: 0.0476 - val_loss: 0.4769 - val_accuracy: 0.1429
Epoch 49/50
1/1 [==============================] - 0s 428ms/step - loss: 0.5478 - accuracy: 0.0476 - val_loss: 0.4769 - val_accuracy: 0.1429
Epoch 50/50
1/1 [==============================] - ETA: 0s - loss: 0.5482 - accuracy: 0.0476WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.509478). Check your callbacks.
1/1 [==============================] - 0s 417ms/step - loss: 0.5482 - accuracy: 0.0476 - val_loss: 0.4772 - val_accuracy: 0.1429
1 Physical GPUs, 1 Logical GPU
WARNING:tensorflow:Layer lstm_16 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
WARNING:tensorflow:Layer lstm_17 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
WARNING:tensorflow:Layer lstm_18 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
WARNING:tensorflow:Layer lstm_19 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
1/1 [==============================] - 0s 67ms/step - loss: 0.6931 - accuracy: 0.0714
---------- Untrained model, accuracy: 7.14% ----------
WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
WARNING:tensorflow:Layer lstm_3 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
1/1 [==============================] - 0s 3ms/step - loss: 0.4772 - accuracy: 0.1429
---------- Restored model, accuracy: 14.29% ----------
数据集的形状：
X_train: (9538, 1, 7)
y_train: (9538,)
X_test: (3179, 1, 7)
y_test (3179,)