Commit bb4fb9f4 authored by Rudolf Chrispens's avatar Rudolf Chrispens
Browse files

initial python notebook commit

parent 7fe3ce3c

LSTM.ipynb

0 → 100644
+34 −0
Original line number Diff line number Diff line 
%% Cell type:code id: tags:



``` python

# required dependecies

```



%% Cell type:code id: tags:



``` python

# imports

```



%% Cell type:code id: tags:



``` python

# Configuration of LSTM

```



%% Cell type:code id: tags:



``` python

# Load Data and show attention sample

```



%% Cell type:code id: tags:



``` python

# Use validation data calculate Reference values

```



%% Cell type:code id: tags:



``` python

```

caption-lib/lstm/Validators/coco_validator.py

+4 −4
Original line number Diff line number Diff line
@@ -86,9 +86,9 @@ class coco_validator(object): 
            else:

                image_path = _config.path.image_data_path + _config.path.image_validation



            """

            if attention_visualization:

                for n in range(10):



            if _config.validator.show_attention:

                for n in range(_config.validator.attention_sample_number):

                    print("#>\tSampled Caption: %s" % decoded[n])



                    # Plot original image
@@ -123,7 +123,7 @@ class coco_validator(object): 
                        plt.axis('off')



                    plt.show()

            """





            if save_sampled_captions:

                num_iter = int(np.ceil(features.shape[0] / self.batch_size)) - self.batch_size

caption-lib/lstm/configuration.py

+7 −5
Original line number Diff line number Diff line
@@ -38,22 +38,24 @@ class default_reader(object): # default reader config 
    # current_reader = "basic_text_reader.py"



class default_validator(object):

    validator_on = True

    validator_on = False

    verbose = False

    attention_sample_number = 1

    show_attention = False



class default_trainer(object):

    train_on = True

    train_on = False

    train_fresh_and_override = True

    verbose = False

    num_layers = 2

    num_layers = 1

    num_epochs = 60

    num_steps = 35

    vector_size = 2048

    batch_size = 20

    dropout_on = True

    dropout = 0.5

    init_scale = 0.05

    forget_bias = 1.0

    # init_scale = 0.05

    forget_bias = 0.4

    hidden_size = 1024

    learning_rate = 0.001