Question

我是Tensorflow中的新手，请尝试以下给出的线性回归代码：

{!! Form::model($trade,['method'=>'PATCH', 'action'=> ['trades\AddSingleTradeController@update',$trade->id]]) !!}


<div class="col-sm-10">

    <div class="form-group col-sm-5">
        {!! Form::label('exchange_id', 'Exchanges:') !!}
        {!! Form::select('exchange_id', [''=>'Choose Options'] + $exchanges , null, ['class'=>'form-control'])!!}
    </div>

    <div class="form-group col-sm-5">
        {!! Form::label('market_id', 'Markets:') !!}
        {!! Form::select('market_id', [''=>'Choose Options'] + $markets, null, ['class'=>'form-control'])!!}
    </div>

    <div class="form-group col-sm-10">
        {!! Form::label('symbol_id', 'Symbols:') !!}
        {!! Form::select('symbol_id', [''=>'Choose Options']+ $symbals   , null, ['class'=>'form-control'])!!}
    </div>

    <div class="form-group col-sm-10">

        {{ Form::radio('is_action', 1) }} Buy
        {{ Form::radio('is_action', 0) }} Sell
    </div>

    <div class="form-group col-lg-5">
        {!! Form::label('tradedate', 'Traded date:') !!}
        {!! Form::date('tradedate', null, ['class'=>'form-control'])!!}
    </div>

    <div class="form-group col-lg-5">
        {!! Form::label('rate', 'Traded Rate:') !!}
        {!! Form::text('rate', null, ['class'=>'form-control'])!!}
    </div>


    <div class="form-group col-sm-10">
        {!! Form::label('reason', 'Choose Reasons:') !!}
        {{Form::select('reason',$reasons,null, array('id'=>'reasons','multiple'=>'multiple','name'=>'reason[]',"class"=>"js-example-basic-multiple form-control", 'data-width'=>'60%', 'data-live-search'=>'true','onchange' => 'all_function()'))}}

    </div>


    <div class="form-group col-lg-10">
        {!! Form::label('note', 'Note:') !!}
        {!! Form::textarea('note', null, ['class'=>'form-control', 'rows' => 2, 'cols' => 40])!!}
    </div>



    <div class="form-group col-lg-4">
        {!! Form::submit('Save', ['class'=>'btn btn-success btn-lg']) !!}
    </div>



    {!! Form::close() !!}




    <div class="form-group col-lg-4">
    {!! Form::open(['method'=>'DELETE', 'action'=> ['trades\AddSingleTradeController@destroy', $trade->id]]) !!}

    <div class="form-group">
        {!! Form::submit('Delete', ['class'=>'btn btn-danger btn-lg']) !!}
    </div>

    </div>

    {!! Form::close() !!}

当循环运行时：

<td><a href="{{route('member.add-single-trade.edit', $trade->id)}}">{{$trade->stoploss}}</a></td>

给我错误

我真的不明白为什么import tensorflow as tf import numpy as np import pandas as pd from sklearn.datasets import load_boston import matplotlib.pyplot as plt boston=load_boston() type(boston) boston.feature_names bd=pd.DataFrame(data=boston.data,columns=boston.feature_names) bd['Price']=pd.DataFrame(data=boston.target) np.random.shuffle(bd.values) W=tf.Variable(0.0) b=tf.Variable(0.0) #print(bd.shape[1]) tf.summary.histogram('Weights', W) tf.summary.histogram('Biases', b) dataset_input=bd.iloc[:, 0 : bd.shape[1]-1]; #dataset_input.head(2) dataset_output=bd.iloc[:, bd.shape[1]-1] dataset_output=dataset_output.values dataset_output=dataset_output.reshape((bd.shape[0],1)) #converted (506,) to (506,1) because in pandas #the shape was not changing and it was needed later in feed_dict dataset_input=dataset_input.values #only dataset_input is in DataFrame form and converting it into np.ndarray X=tf.placeholder(tf.float32, shape=(None,bd.shape[1]-1)) Y=tf.placeholder(tf.float32, shape=(None,1)) Y_=W*X+b print(X.shape) print(Y.shape) loss=tf.reduce_mean(tf.square(Y_-Y)) tf.summary.scalar('loss',loss) optimizer=tf.train.GradientDescentOptimizer(0.5) train=optimizer.minimize(loss) init=tf.global_variables_initializer()#tf.global_variables_initializer()#tf.initialize_all_variables() sess=tf.Session() sess.run(init) wb_=[] with tf.Session() as sess: summary_merge = tf.summary.merge_all() writer=tf.summary.FileWriter("Users/ajay/Documents",sess.graph) epochs=10 sess.run(init) for i in range(epochs): s_mer=sess.run(summary_merge,feed_dict={X:dataset_input,Y:dataset_output}) #ERROR________ERROR sess.run(train,feed_dict={X:dataset_input,Y:dataset_output}) sess.run(loss) writer.add_summary(s_mer,i) #tf.summary.histogram(name="loss",values=loss) if(i%5==0): print(i, sess.run([W,b])) wb_.append(sess.run([W,b])) print(writer.get_logdir()) print(writer.close())出现在上面的行中。如果不存在该怎么办？

此外，为什么我们将s_mer=sess.run(summary_merge,feed_dict={X:dataset_input,Y:dataset_output})传递到add_summary（）中？我不了解它的作用。我们不能只将“ summary_merge”传递到add_summary（）中。

请说明add_summary（）的作用。

我在IPython NB中遇到的错误是：

feed_dict

Answer 1

问题在于变量dataset_input和dataset_output的类型为float64，但您使用占位符float32，但它们必须相同。 dataset_input = np.array(dataset_input, dtype=np.float32)和dataset_output = np.array(dataset_output, dtype=np.float32)对我的计算机有帮助。

然后，在调用sess.run(loss)时，您还需要添加feed_dict={X:dataset_input,Y:dataset_output}，因为它使用X和Y占位符。

我真的不明白为什么在上一行中存在feed_dict。如果不存在会发生什么？

您需要使用feed_dict将数据提供给占位符。如果您不向其提供任何数据，则会发生类似的错误，因为它没有用于计算的数据。

希望有帮助。

InvalidArgumentError：您必须使用dtype float输入占位符张量'Placeholder_56'的值

1 个答案: