Learn more about Teams. Using KerasClassifier for training neural network Ask Question. Asked 8 months ago. Modified 8 months ago. Viewed times. At this point I got the following error message: ValueError: Expected 2D array, got 1D array instead: Reshape your data either using array.

reshape -1, 1 if your data has a single feature or array. reshape 1, -1 if it contains a single sample. layers import Input, Dropout, Dense from tensorflow. fit np. reshape -1,1 , np. machine-learning keras neural-network nlp bert-language-model. asked Feb 27 at lazarea lazarea 1, 10 10 silver badges 29 29 bronze badges. scikeras doesn't seem to accept not numerical input: github. Add a comment. Sorted by: Reset to default.

Highest score default Trending recent votes count more Date modified newest first Date created oldest first. I managed to solve it! For several decades now, those in the artificial intelligence community have used the neural network model in creating computers that 'think' and 'learn' based on the outcomes of their actions.

Unlike the traditional data structure, neural networks take in multiple streams of data and output one result. If there's a way to quantify the data, there's a way to add it to the factors being considered in making a prediction. They're often used in Forex market prediction software because the network can be trained to interpret data and draw a conclusion from it.

Before they can be of any use in making Forex predictions, neural networks have to be 'trained' to recognize and adjust for patterns that arise between input and output. The training and testing can be time consuming, but is what gives neural networks their ability to predict future outcomes based on past data. The basic idea is that when presented with examples of pairs of input and output data, the network can 'learn' the dependencies, and apply those dependencies when presented with new data.

From there, the network can compare its own output to see how close to correct the prediction was, and go back and adjust the weight of the various dependencies until it reaches the correct answer. This requires that the network be trained with two separate data sets — the training and the testing set. One of the strengths of neural networks is that it can continue to learn by comparing its own predictions with the data that is continually fed to it.

Neural networks are also very good at combining both technical and fundamental data, thus making a best of both worlds scenario. Their very power allows them to find patterns that may not have been considered, and apply those patterns to prediction to come up with uncannily accurate results. The Neural Network Classifier uses the Sci-Kit Learn Machine Learning Python library distributed with Anaconda.

No offer or solicitation to buy or sell securities, securities derivatives, futures products or off-exchange foreign currency forex transactions of any kind, or any type of trading or investment advice, recommendation or strategy, is made, given or in any manner endorsed by panchamAI.

You are fully responsible for any investment or trading decisions you make. Futures and options trading has a large potential risk. You must be aware of the risks and be willing to accept them in order to invest in the futures and options markets. No representation is being made that any account will or is likely to achieve profits or losses similar to those discussed in this product.

Past performance, whether actual or indicated by historical tests of strategies, is no guarantee of future performance or success. No warranties of profitability are being made or given. There is a possibility that you may sustain a loss equal to or greater than your entire investment regardless of which asset class you trade equities, options, futures or forex ; therefore, you should not invest or risk money that you cannot afford to lose.

Hypothetical or simulated performance results have certain inherent limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not actually been executed, the results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity.

Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight.

Deep learning has substantially advanced the state of the art in computer vision, natural language processing, and other fields. The paper examines the potential of deep learning for exchange rate forecasting. We systematically compare long short-term memory networks and gated recurrent units to traditional recurrent network architectures as well as feedforward networks in terms of their directional forecasting accuracy and the profitability of trading model predictions.

Empirical results indicate the suitability of deep networks for exchange rate forecasting in general but also evidence the difficulty of implementing and tuning corresponding architectures. Especially with regard to trading profit, a simpler neural network may perform as well as if not better than a more complex deep neural network. Deep learning has revitalized research into artificial neural networks. Substantial methodological advancements associated with the optimization and regularization of large neural networks, the availability of large data sets together with the computational power to train large networks, and development of powerful, easy-to-use software libraries, deep neural networks DNNs have achieved breakthrough performance in computer vision, natural language processing, and other domains LeCun et al.

A feature that sets deep learning apart from conventional machine learning is the ability automatically extract discriminative features from raw data Nielsen Reducing the need for manual feature engineering, this ability decreases the costs of applying a learning algorithm in industry, simplifies tasks associated with model maintenance, and, more generally, broadens the scope of deep learning applications. Convolutional neural networks and recurrent neural networks RNNs have been particularly successful.

The former represent the model of choice for computer vision tasks. RNNs are designed for processing sequential data including natural language, audio, and generally, any type of time series. The paper focuses on RNNs and examines their potential for financial time series forecasting.

Deep-learning-based forecasting models and their applications in finance have attracted considerable attention in recent research Fischer and Krauss ; Huck ; Kim et al. However, only a small number of studies examine the foreign exchange FX market. This is surprising for several reasons. On the one hand, examining the degree to which market developments can be forecast with high accuracy is of academic and practical relevance.

Moreover, exchange rates have been characterized as non-linear, stochastic, and highly non-stationary financial time series Kayacan et al. Finally, the FX market differs notably from other financial markets so that research results from other markets such as stock markets may not generalize to the FX market. The distinct characteristics of the FX market have been discussed in several studies. For example, most participants of the FX market are professional traders Sager and Taylor The FX market also has a higher share of short-term interdealer trading compared to stock markets Lyons Moreover, exchange rates fluctuate enormously leading to doubtful decision-making whether to buy or sell for traders Bagheri et al.

Because of this, models of fair value are less convincing for FX traders compared to stock market traders Taylor ; Campbell et al. Given that the peculiarities question the generalizability of empirical results from other financial markets and the fact that research into state-of-the-art deep learning approaches for exchange rate forecasting is scarce motivate the focal study.

The goal of the paper is to contribute to the empirical literature on FX forecasting by re-introducing deep machine learning-based forecasting methodology to the community and assessing the accuracy of corresponding models when forecasting exchange rate movements. In particular, the paper reports original results from a comparative analysis of Long Short-Term Memory LSTM and Gated Recurrent Unit GRU neural networks versus benchmark models.

Considering four exchange rates, we assess forecasting models in terms of their directional accuracy and the profitability of trading on model predictions.

Our analysis of related literature see Sect. Therefore, we consider the empirical results reported in the paper a valuable and needed update of the state of affairs at the interface of deep learning and FX rate forecasting. Given the scarcity of deep learning-based approaches in the FX modeling literature, a secondary contribution of the paper may be seen in the fact that it provides a thorough explanation of the operating principles of RNNs vis-a-vis conventional feedforward neural networks FNNs and re-introduces recent RNN representatives in the form of LSTM and GRU to the FX modeling literature.

In this scope, we also elaborate on the ways in which these recent RNNs overcome the vanishing gradient problem, which rendered previous attempts to train deep RNNs less effective. The paper is organized as follows: the next section elaborates on neural network-based forecasting and introduces LSTM and GRU. Thereafter, we review related work and show how the paper contributes to closing gaps in research. Subsequently, we describe the experimental design of the forecasting comparison and report empirical results.

We then conclude the paper with a summary and discussion of findings and an outlook to future research. Neural networks consist of multiple connected layers of computational units called neurons.

The network receives input signals and computes an output through a concatenation of matrix operations and non-linear transformations. In this paper, the input represents time series data and the output a price forecast.

Every neural network consists of one input and one output layer, and one or multiple hidden layers, whereby each layer consists of several neurons. The connections between the neurons of different layers carry a weight.

Network training refers to the tuning of these weights in such a way that network output i. The training of a neural network through adjusting connection weights is equivalent to the task of estimating a statistical model through empirical risk minimization Härdle and Leopold The layer of a FNN comprises fully connected neurons without shortcuts or feedback loops. When processing sequential data, the activation of a hidden layer unit in an FNN at time t can be described as follows:.

As such, the prediction is again only a function of inputs, weights, and biases. A FNN will treat input sequences as time-invariant data and thus be agnostic of inherent features of time series. RNNs are designed for sequential data processing. To this end, they include feedback loops and feed the output signal of a neuron back into the neuron. When viewed over time, RNNs resemble a chain-like sequence of copies of neural networks, each passing on information to its successor.

The backpropagation algorithm uses the insight that the gradient with respect to each weight can be found by starting at the gradient with respect to the output and then propagating the derivatives backwards through the network using the chain rule Rumelhart et al. In RNNs, the gradient descent-based training of the network is called backpropagation through time, as the error derivatives are not only backpropagated through the network itself but also back through time via the recurrent connections Werbos The derivative of both lies in the interval [0, 1] and thus any gradient with respect to a weight that feeds into such an activation function is bound to be squeezed smaller Hochreiter b.

Given that we are successively computing the derivative of an activation function by use of the chain rule, the gradient gets smaller and smaller the further away a weight is from the output layer see Fig. In RNNs, this problem, routinely called the problem of gradient vanishing, is amplified by the sequential data processing of the network. Specifically, the gradient signal vanishes not only across layers but also across time steps. In consequence, RNNs face difficulties in modeling long-term dependencies, and much research has sought ways to overcome this issue Schaefer et al.

The problem of gradient vanishing also implies that the magnitude of weight adjustments during training decreases for those weights. Effectively, weights in early layers learn much slower than those in late hidden layers closer to the output Nielsen Note that the recursive application of the chain rule in neural network training may also cause a problem closely related to that of gradient vanishing.

This problem is called gradient explosion and it occurs when recursively multiplying weight matrices with several entries above one in the backward pass of network training.

Remedies of this problem, which help to stabilize and accelerate neural network training, include gradient clipping and batch normalization Goodfellow et al. One solution to the vanishing gradient problem was proposed by Hochreiter and Schmidhuber in the form of LSTM.

Twenty years after its invention, LSTM and its variants have turned out to become a state-of-the-art neural network architecture for sequential data.

The following discussion of the LSTM cell follows Graves as it seems to be one of the most popular LSTM architectures in recent research and is also available in the widely used Python library Keras Chollet et al. The central feature that allows LSTM to overcome the vanishing gradient problem is an additional pathway called the cell state. The cell state is a stream of information that is passed on through time. Its gradient does not vanish and enforces a constant error flow through time Hochreiter a.

The cell state allows the LSTM to remember dependencies through time and facilitates bridging long time lags Hochreiter and Schmidhuber Figure 2 depicts a single LSTM cell with all but the cell state pathway grayed out. Note that the cell state contains no activation functions but only linear operations. The following discussion details how the cell state is maintained, updated, or read. A single LSTM unit a memory block according to Olah with all but the cell state pathway grayed out.

The LSTM cell contains a number of gate structures that allow accessing the cell. It takes the weighted current and recurrent inputs and maps them to the interval [0, 1]. As shown in Fig. The objective of this gate is to protect the information of the cell state, which has accumulated over previous time steps, from irrelevant updates.

Therefore, the input gate selectively updates the cell state with new information Hochreiter and Schmidhuber It is not exactly the parts that got remembered that get updated, and not exactly the ones that were forgotten either. As in a FNN, predictions are computed from the hidden state by applying an output activation in the final layer. A typical LSTM cell with forget gate, input gate, and output gate is depicted in Fig. The different gates and activations work together to save, keep, and produce information for the task at hand.

A sequence of LSTM units through time Olah This architecture is an augmented version of the original LSTM architecture and the setup most common in the literature Greff et al.

Figure 7 , which depicts a sequence of LSTM cells through time, conveys how information can be propagated through time via the cell state. Preservation of gradient information by LSTM Graves There exist a few variants of the LSTM cell with fewer or additional components. For example, one modification concerns the use of peephole connections , which allow the cell state to control the gates and have been shown to increase LSTM resilience toward spikes in time series Gers and Schmidhuber Greff et al.

They start from an LSTM cell with all gates and all possible peephole connections and selectively remove one component, always testing the resulting architecture on data from several domains. The empirical results suggest that the forget gate and the output activation function seem particularly important, while none out of the investigated modifications of the above LSTM cell significantly improves performance Greff et al.

In view of these findings, we focus on the LSTM as described above. A second approach to overcome the vanishing gradient problem in RNNs is GRUs Cho et al. They also use gates but simplify the handling of the cell state. It decides how much of the recurrent information is kept:. A reset gate controls to which extent the recurrent hidden state is allowed to feed into the current activation:. The new activation can be computed as. Figure 8 illustrates that GRUs have less parameters than LSTMs, which should make them computationally more efficient.

In terms of forecasting performance, previous results on GRUs versus LSTMs are inconclusive see, e.

Kamijo, K. This resulted in many instances of returns close to zero and few, but relatively large deviations and could have lead to the models exhibiting low confidence in their predictions. The following discussion details how the cell state is maintained, updated, or read. Another benefit of currency trading systems based on neural networks is their ability to use intelligence without being influenced by emotion. Lately, neural networks have been attracting attention in the commercial community.

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