Bdtjtk

I'm trying to do some regression analysis on some flight data. I loaded my parameters (previous .mat files) and now I have 6 parameters as predictors in the format list and one response parameter as well in format list.

What I did so far is combining the predictors into one combined list. Now I wanted to create a model and train it but as I expected, the format list is not compatibel. The error I got is: AttributeError: 'list' object has no attribute 'shape'

Thus I tried to convert the lists into a tensor with tf.convert_to_tensor. Unfortunately it is not working. The error I receive now is: ValueError: setting an array element with a sequence

Unfortunately I only learned how to do image classification with CNNs in tensorflow and I'm not sure which format is the right one to give my data to the input layer. Right now my Predictors consists of 6 Parameters taken every 0.1 seconds. The list is shaped like this:

[[('__header__', b'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Wed Dec 26 19:17:10 2018'), ('__version__', '1.0'), ('__globals__', ), ('RSVRQuantity_Center', array([[111],

       [111],

       [111],

       ...,

       [104],

       [104],

       [104]], dtype=uint8))], [('__header__', b'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Wed Dec 26 19:32:29 2018'), ('__version__', '1.0'), ('__globals__', ), ('RSVRTemp_Center', array([[36.5  ],

       [36.5  ],

       [36.5  ],

       ...,

       [ 4.625],

       [ 4.625],

       [ 4.625]]))], [('__header__', b'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Wed Dec 26 19:33:00 2018'), ('__version__', '1.0'), ('__globals__', ), ('AirDrivenPumpTemp1_Center', array([[ 35.9 ],

       [ 35.9 ],

       [ 35.9 ],

       ...,

       [-13.75],

       [-13.75],

       [-13.75]]))], [('__header__', b'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Wed Dec 26 19:33:29 2018'), ('__version__', '1.0'), ('__globals__', ), ('AirDrivenPumpTemp2_Center', array([[ 35.9 ],

       [ 35.9 ],

       [ 35.9 ],

       ...,

       [-13.13],

       [-13.13],

       [-13.13]]))], [('__header__', b'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Wed Dec 26 19:34:12 2018'), ('__version__', '1.0'), ('__globals__', ), ('ElecPumpTemp1_Center', array([[45.9 ],

       [45.9 ],

       [45.9 ],

       ...,

       [14.63],

       [14.63],

       [14.63]]))], [('__header__', b'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Wed Dec 26 19:33:54 2018'), ('__version__', '1.0'), ('__globals__', ), ('ElecPumpTemp2_Center', array([[43.6 ],

       [43.6 ],

       [43.6 ],

       ...,

       [12.75],

       [12.75],

       [12.75]]))]]

The model I created so far looks like this:

def GradientDescent(features, labels, mode):



    # create an dense layer as input layer with 6 units for 6 features and ReLU activation function

    input_layer = tf.layers.dense(inputs=features["x"], units=6, activation=tf.nn.relu)



    # declare a dense layer with 12 units and ReLU activation function

    hidden1 = tf.layers.dense(inputs=input_layer, units=12, activation=tf.nn.relu)



    # declare a dense layer with 12 units and ReLU activation function

    hidden2 = tf.layers.dense(inputs=hidden1, units=12, activation=tf.nn.relu)



    # declare a dense layer with 6 units and ReLU activation function

    hidden3 = tf.layers.dense(inputs=hidden2, units=6, activation=tf.nn.relu)



    # declare a dense layer with 1 unit as output layer

    logits = tf.layers.dense(inputs=hidden3, units=1)



    predictions = tf.argmax(input=logits, axis=1)



    # return the estimator when predicting (no loss and training function needs to be defined here)

    if mode == tf.estimator.ModeKeys.PREDICT:

        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)



    # declare the loss function sparse softmax cross entropy

    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels,logits=logits)



    # declare a decreasing learning rate

    # starter_learning_rate = 0.1

    # global_step = tf.Variable(0, trainable=False)

    # learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step, 10000, 0.96, staircase=True)



    # declare a gradient descent optimizer with a decreasing learning rate

    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)



    train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())



    # define the accuracy error metric

    eval_metric_ops = {

        "accuracy": tf.metrics.accuracy(labels=labels, predictions=predictions)

    }



    #tf.summary.scalar('accuracy', accuracy)



    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op, predictions=predictions, eval_metric_ops=eval_metric_ops)

I'm giving the data to train like this:

HydSysPress_Center_PTRTRQ = tf.estimator.Estimator(model_fn=GradientDescent, model_dir="GradDescent_10000")



train_input = tf.estimator.inputs.numpy_input_fn(x={"x": Predictors}, y=HydSysPress_Center, batch_size=64, num_epochs=None, shuffle=False)

HydSysPress_Center_PTRTRQ.train(input_fn=train_input, steps=100)

I want to start training the model without mixing up the columns and rows of the parameters, since every parameter is taken with a fixed time stamp. For every helpful answer I would be very thankful!