The poem by Robert Frost "Stopping by words on the Snowing Evening" was converted from PDF to text, pre-processed, and tokenized using word indexes. It was transformed into 50-word sequences. An LSTM model with a vocabulary size and vector space of 50 was trained for 200 epochs, achieving 84.83 accuracy.
Automatic text generation is the generation of natural language texts by computer. It has applications in automatic documentation systems, automatic letter writing, automatic report generation, etc. In this project, we are going to generate words given a set of input words. We are going to train the LSTM model using Robert Frost poem Stopping by woods in a snowing evenining which is taken in pdf format and then converted into text file.
https://drive.google.com/file/d/1ZdSH2PJZROCHjz3PYKd7AtnzicbxWSh1/view?usp=sharing
Stopping by Woods on a Snowy Evening Robert Frost
Whose woods these are I think I know
His house is in the village though
He will not see me stopping here
To watch his woods fill up with snow
My little horse must think it queer
To stop without a farmhouse near
Between the woods and frozen lake
The darkest evening of the year
He gives his harness bells a shake
To ask if there is some mistake
The only other sounds the sweep
Of easy wind and downy flake
The woods are lovely dark and deep
But I have promises to keep
And miles to go before I sleep
And miles to go before I sleep
Admiring Light on a Sunny Day Erika Fitzpatrick
What light this is I may it know
Its beams barred by finite time though
He should not mind me pausing now
To admire this light ere it go
My wearied mind considers how
There is time enough to allow
Dead and dilated eyes to gaze
On light thats not for me endowed
It filters through in timid haze
For this room its not seen in days
Dust dances where lighted day glows
In mute music and golden rays
Sunlight is happy hope arose
But I have ssignments to close
And pages to rove before I doze
And pages to rove before I doze
- Long Short-Term Memory (LSTM) networks are a modified version of recurrent neural networks, which makes it easier to remember past data in memory.
- Generally LSTM is composed of a cell (the memory part of the LSTM unit) and three "regulators", usually called gates, of the flow of information inside the LSTM unit: an input gate, an output gate and a forget gate.
- Intuitively, the cell is responsible for keeping track of the dependencies between the elements in the input sequence.
- The input gate controls the extent to which a new value flows into the cell, the forget gate controls the extent to which a value remains in the cell and the output gate controls the extent to which the value in the cell is used to compute the output activation of the LSTM unit.
- The activation function of the LSTM gates is often the logistic sigmoid function.
- There are connections into and out of the LSTM gates, a few of which are recurrent. The weights of these connections, which need to be learned during training, determine how the gates operate.
['stopping', 'by', 'woods', 'on', 'a', 'snowy', 'evening', 'robert', 'frost', 'whose', 'woods', 'these', 'are', 'i', 'think', 'i', 'know', 'his', 'house', 'is', 'in', 'the', 'village', 'though', 'he', 'will', 'not', 'see', 'me', 'stopping', 'here', 'to', 'watch', 'his', 'woods', 'fill', 'up', 'with', 'snow', 'my', 'little', 'horse', 'must', 'think', 'it', 'queer', 'to', 'stop', 'without', 'a']
Create a unique numerical token for each unique word in the dataset. fit_on_texts() updates internal vocabulary based on a list of texts. texts_to_sequences() transforms each text in texts to a sequence of integers.sequences containes a list of integer values created by tokenizer. Each line in sequences has 51 words. Now we will split each line such that the first 50 words are in X and the last word is in y.
array([112, 36, 11, 17, 9, 136, 35, 134, 133, 132, 11, 131, 33,
4, 34, 4, 31, 18, 126, 7, 13, 3, 122, 29, 15, 118,
8, 116, 14, 112, 113, 1, 110, 18, 11, 106, 104, 103, 101,
27, 100, 98, 97, 34, 6, 94, 1, 92, 90, 9])
Vocab Size is 137 for my dataset Sequence Length is of 50
A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor.
The Embedding layer is initialized with random weights and will learn an embedding for all of the words in the training dataset. It requires 3 arguments:
- input_dim: This is the size of the vocabulary in the text data which is vocab_size in this case.
- output_dim: This is the size of the vector space in which words will be embedded. It defines the size of the output vectors from this layer for each word.
- input_length: Length of input sequences which is seq_length.
This is the main layer of the model. It learns long-term dependencies between time steps in time series and sequence data. return_sequence when set to True returns the full sequence as the output.
Dense layer is the regular deeply connected neural network layer. It is the most common and frequently used layer. The rectified linear activation function or relu for short is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero.
The last layer is also a dense layer with 13009 neurons because we have to predict the probabilties of 13009 words. The activation function used is softmax. Softmax converts a real vector to a vector of categorical probabilities. The elements of the output vector are in range (0, 1) and sum to 1.
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 50, 50) 6850
lstm (LSTM) (None, 50, 100) 60400
lstm_1 (LSTM) (None, 100) 80400
dense (Dense) (None, 100) 10100
dense_1 (Dense) (None, 137) 13837
=================================================================
Total params: 171,587
Trainable params: 171,587
Non-trainable params: 0
_________________________________________________________________
Model is Trained at 200 epochs at batch size of 16 and achieved a accuracy of 84.83 %. After training the model is saved using h5 extension and is ready for prediction or Text generation.
model.fit(X, y, batch_size = 32, epochs = 200)
Epoch 1/200
6/6 [==============================] - 6s 99ms/step - loss: 4.9193 - accuracy: 0.0169
Epoch 2/200
6/6 [==============================] - 1s 142ms/step - loss: 4.9043 - accuracy: 0.0449
Epoch 3/200
6/6 [==============================] - 1s 252ms/step - loss: 4.8186 - accuracy: 0.0393
Epoch 4/200
6/6 [==============================] - 2s 304ms/step - loss: 4.7181 - accuracy: 0.0449
Epoch 5/200
6/6 [==============================] - 2s 291ms/step - loss: 4.6537 - accuracy: 0.0562
Epoch 6/200
6/6 [==============================] - 2s 249ms/step - loss: 4.6114 - accuracy: 0.0562
Epoch 7/200
6/6 [==============================] - 1s 219ms/step - loss: 4.5836 - accuracy: 0.0393
Epoch 8/200
6/6 [==============================] - 1s 172ms/step - loss: 4.5602 - accuracy: 0.0225
Epoch 9/200
6/6 [==============================] - 1s 215ms/step - loss: 4.5394 - accuracy: 0.0562
Epoch 10/200
6/6 [==============================] - 1s 187ms/step - loss: 4.5183 - accuracy: 0.0562
Epoch 11/200
6/6 [==============================] - 1s 151ms/step - loss: 4.4917 - accuracy: 0.0562
Epoch 12/200
6/6 [==============================] - 1s 94ms/step - loss: 4.4669 - accuracy: 0.0449
Epoch 13/200
6/6 [==============================] - 1s 96ms/step - loss: 4.4229 - accuracy: 0.0506
Epoch 14/200
6/6 [==============================] - 1s 94ms/step - loss: 4.3878 - accuracy: 0.0506
Epoch 15/200
6/6 [==============================] - 1s 96ms/step - loss: 4.3356 - accuracy: 0.0506
Epoch 16/200
6/6 [==============================] - 1s 133ms/step - loss: 4.2711 - accuracy: 0.0562
Epoch 17/200
6/6 [==============================] - 1s 95ms/step - loss: 4.2217 - accuracy: 0.0562
Epoch 18/200
6/6 [==============================] - 1s 94ms/step - loss: 4.1446 - accuracy: 0.0562
Epoch 19/200
6/6 [==============================] - 1s 111ms/step - loss: 4.0780 - accuracy: 0.0899
Epoch 20/200
6/6 [==============================] - 1s 153ms/step - loss: 3.9843 - accuracy: 0.0955
Epoch 21/200
6/6 [==============================] - 1s 157ms/step - loss: 3.9270 - accuracy: 0.0843
Epoch 22/200
6/6 [==============================] - 1s 163ms/step - loss: 3.8681 - accuracy: 0.0955
Epoch 23/200
6/6 [==============================] - 1s 149ms/step - loss: 3.7527 - accuracy: 0.0843
Epoch 24/200
6/6 [==============================] - 1s 159ms/step - loss: 3.6529 - accuracy: 0.1124
Epoch 25/200
6/6 [==============================] - 1s 158ms/step - loss: 3.5730 - accuracy: 0.0843
Epoch 26/200
6/6 [==============================] - 1s 154ms/step - loss: 3.4857 - accuracy: 0.1124
Epoch 27/200
6/6 [==============================] - 1s 94ms/step - loss: 3.3912 - accuracy: 0.1180
Epoch 28/200
6/6 [==============================] - 1s 96ms/step - loss: 3.2802 - accuracy: 0.1124
Epoch 29/200
6/6 [==============================] - 1s 93ms/step - loss: 3.2172 - accuracy: 0.1067
Epoch 30/200
6/6 [==============================] - 1s 98ms/step - loss: 3.1472 - accuracy: 0.1124
Epoch 31/200
6/6 [==============================] - 1s 95ms/step - loss: 3.0373 - accuracy: 0.1124
Epoch 32/200
6/6 [==============================] - 1s 97ms/step - loss: 3.0024 - accuracy: 0.1236
Epoch 33/200
6/6 [==============================] - 1s 95ms/step - loss: 2.9254 - accuracy: 0.1236
Epoch 34/200
6/6 [==============================] - 1s 93ms/step - loss: 2.8419 - accuracy: 0.1461
Epoch 35/200
6/6 [==============================] - 1s 98ms/step - loss: 2.8271 - accuracy: 0.1236
Epoch 36/200
6/6 [==============================] - 1s 94ms/step - loss: 2.7117 - accuracy: 0.1461
Epoch 37/200
6/6 [==============================] - 1s 97ms/step - loss: 2.6796 - accuracy: 0.1461
Epoch 38/200
6/6 [==============================] - 1s 95ms/step - loss: 2.6112 - accuracy: 0.1685
Epoch 39/200
6/6 [==============================] - 1s 96ms/step - loss: 2.5577 - accuracy: 0.1742
Epoch 40/200
6/6 [==============================] - 1s 95ms/step - loss: 2.4924 - accuracy: 0.2022
Epoch 41/200
6/6 [==============================] - 1s 93ms/step - loss: 2.4705 - accuracy: 0.1966
Epoch 42/200
6/6 [==============================] - 1s 101ms/step - loss: 2.4125 - accuracy: 0.1854
Epoch 43/200
6/6 [==============================] - 1s 93ms/step - loss: 2.3626 - accuracy: 0.1910
Epoch 44/200
6/6 [==============================] - 1s 156ms/step - loss: 2.3505 - accuracy: 0.2191
Epoch 45/200
6/6 [==============================] - 1s 149ms/step - loss: 2.3166 - accuracy: 0.2247
Epoch 46/200
6/6 [==============================] - 1s 154ms/step - loss: 2.2827 - accuracy: 0.2079
Epoch 47/200
6/6 [==============================] - 1s 156ms/step - loss: 2.2730 - accuracy: 0.1966
Epoch 48/200
6/6 [==============================] - 1s 157ms/step - loss: 2.2693 - accuracy: 0.2022
Epoch 49/200
6/6 [==============================] - 1s 162ms/step - loss: 2.1978 - accuracy: 0.2247
Epoch 50/200
6/6 [==============================] - 1s 158ms/step - loss: 2.1997 - accuracy: 0.2191
Epoch 51/200
6/6 [==============================] - 1s 111ms/step - loss: 2.1650 - accuracy: 0.2360
Epoch 52/200
6/6 [==============================] - 1s 94ms/step - loss: 2.1340 - accuracy: 0.2191
Epoch 53/200
6/6 [==============================] - 1s 92ms/step - loss: 2.0931 - accuracy: 0.2753
Epoch 54/200
6/6 [==============================] - 1s 95ms/step - loss: 2.0811 - accuracy: 0.2472
Epoch 55/200
6/6 [==============================] - 1s 95ms/step - loss: 2.0293 - accuracy: 0.3090
Epoch 56/200
6/6 [==============================] - 1s 101ms/step - loss: 2.0077 - accuracy: 0.2809
Epoch 57/200
6/6 [==============================] - 1s 94ms/step - loss: 2.0220 - accuracy: 0.2809
Epoch 58/200
6/6 [==============================] - 1s 95ms/step - loss: 1.9995 - accuracy: 0.2865
Epoch 59/200
6/6 [==============================] - 1s 99ms/step - loss: 1.9635 - accuracy: 0.2753
Epoch 60/200
6/6 [==============================] - 1s 96ms/step - loss: 1.9456 - accuracy: 0.2865
Epoch 61/200
6/6 [==============================] - 1s 97ms/step - loss: 1.9000 - accuracy: 0.3090
Epoch 62/200
6/6 [==============================] - 1s 93ms/step - loss: 1.8796 - accuracy: 0.3371
Epoch 63/200
6/6 [==============================] - 1s 98ms/step - loss: 1.8552 - accuracy: 0.3315
Epoch 64/200
6/6 [==============================] - 1s 95ms/step - loss: 1.9291 - accuracy: 0.2865
Epoch 65/200
6/6 [==============================] - 1s 96ms/step - loss: 1.9085 - accuracy: 0.2978
Epoch 66/200
6/6 [==============================] - 1s 98ms/step - loss: 1.8884 - accuracy: 0.3427
Epoch 67/200
6/6 [==============================] - 1s 93ms/step - loss: 1.8393 - accuracy: 0.3371
Epoch 68/200
6/6 [==============================] - 1s 127ms/step - loss: 1.7714 - accuracy: 0.3652
Epoch 69/200
6/6 [==============================] - 1s 152ms/step - loss: 1.7653 - accuracy: 0.3483
Epoch 70/200
6/6 [==============================] - 1s 157ms/step - loss: 1.7249 - accuracy: 0.4326
Epoch 71/200
6/6 [==============================] - 1s 155ms/step - loss: 1.7065 - accuracy: 0.4382
Epoch 72/200
6/6 [==============================] - 1s 158ms/step - loss: 1.7066 - accuracy: 0.3539
Epoch 73/200
6/6 [==============================] - 1s 158ms/step - loss: 1.7310 - accuracy: 0.3820
Epoch 74/200
6/6 [==============================] - 1s 158ms/step - loss: 1.6968 - accuracy: 0.3876
Epoch 75/200
6/6 [==============================] - 1s 140ms/step - loss: 1.6755 - accuracy: 0.4101
Epoch 76/200
6/6 [==============================] - 1s 97ms/step - loss: 1.6827 - accuracy: 0.3708
Epoch 77/200
6/6 [==============================] - 1s 94ms/step - loss: 1.7745 - accuracy: 0.3876
Epoch 78/200
6/6 [==============================] - 1s 97ms/step - loss: 1.7132 - accuracy: 0.3315
Epoch 79/200
6/6 [==============================] - 1s 95ms/step - loss: 1.6812 - accuracy: 0.3315
Epoch 80/200
6/6 [==============================] - 1s 96ms/step - loss: 1.6493 - accuracy: 0.3596
Epoch 81/200
6/6 [==============================] - 1s 95ms/step - loss: 1.6640 - accuracy: 0.3202
Epoch 82/200
6/6 [==============================] - 1s 93ms/step - loss: 1.6891 - accuracy: 0.3483
Epoch 83/200
6/6 [==============================] - 1s 97ms/step - loss: 1.6691 - accuracy: 0.3371
Epoch 84/200
6/6 [==============================] - 1s 94ms/step - loss: 1.6379 - accuracy: 0.3539
Epoch 85/200
6/6 [==============================] - 1s 95ms/step - loss: 1.6103 - accuracy: 0.3539
Epoch 86/200
6/6 [==============================] - 1s 93ms/step - loss: 1.5813 - accuracy: 0.4045
Epoch 87/200
6/6 [==============================] - 1s 99ms/step - loss: 1.5381 - accuracy: 0.3876
Epoch 88/200
6/6 [==============================] - 1s 99ms/step - loss: 1.5375 - accuracy: 0.4270
Epoch 89/200
6/6 [==============================] - 1s 94ms/step - loss: 1.5216 - accuracy: 0.4326
Epoch 90/200
6/6 [==============================] - 1s 96ms/step - loss: 1.4935 - accuracy: 0.4213
Epoch 91/200
6/6 [==============================] - 1s 95ms/step - loss: 1.4759 - accuracy: 0.5112
Epoch 92/200
6/6 [==============================] - 1s 98ms/step - loss: 1.4702 - accuracy: 0.4944
Epoch 93/200
6/6 [==============================] - 1s 157ms/step - loss: 1.4774 - accuracy: 0.4494
Epoch 94/200
6/6 [==============================] - 1s 154ms/step - loss: 1.4802 - accuracy: 0.3876
Epoch 95/200
6/6 [==============================] - 1s 158ms/step - loss: 1.5109 - accuracy: 0.4270
Epoch 96/200
6/6 [==============================] - 1s 155ms/step - loss: 1.4601 - accuracy: 0.4663
Epoch 97/200
6/6 [==============================] - 1s 158ms/step - loss: 1.4627 - accuracy: 0.4494
Epoch 98/200
6/6 [==============================] - 1s 167ms/step - loss: 1.4394 - accuracy: 0.4270
Epoch 99/200
6/6 [==============================] - 1s 159ms/step - loss: 1.5398 - accuracy: 0.3539
Epoch 100/200
6/6 [==============================] - 1s 99ms/step - loss: 1.4874 - accuracy: 0.3933
Epoch 101/200
6/6 [==============================] - 1s 96ms/step - loss: 1.5003 - accuracy: 0.4326
Epoch 102/200
6/6 [==============================] - 1s 99ms/step - loss: 1.4462 - accuracy: 0.4157
Epoch 103/200
6/6 [==============================] - 1s 95ms/step - loss: 1.3994 - accuracy: 0.5056
Epoch 104/200
6/6 [==============================] - 1s 95ms/step - loss: 1.4188 - accuracy: 0.4719
Epoch 105/200
6/6 [==============================] - 1s 99ms/step - loss: 1.4012 - accuracy: 0.4382
Epoch 106/200
6/6 [==============================] - 1s 94ms/step - loss: 1.4000 - accuracy: 0.4944
Epoch 107/200
6/6 [==============================] - 1s 99ms/step - loss: 1.3691 - accuracy: 0.5112
Epoch 108/200
6/6 [==============================] - 1s 96ms/step - loss: 1.3330 - accuracy: 0.5506
Epoch 109/200
6/6 [==============================] - 1s 100ms/step - loss: 1.3167 - accuracy: 0.5562
Epoch 110/200
6/6 [==============================] - 1s 95ms/step - loss: 1.2954 - accuracy: 0.5674
Epoch 111/200
6/6 [==============================] - 1s 94ms/step - loss: 1.2871 - accuracy: 0.5112
Epoch 112/200
6/6 [==============================] - 1s 98ms/step - loss: 1.3029 - accuracy: 0.5337
Epoch 113/200
6/6 [==============================] - 1s 97ms/step - loss: 1.3247 - accuracy: 0.5225
Epoch 114/200
6/6 [==============================] - 1s 99ms/step - loss: 1.3276 - accuracy: 0.5056
Epoch 115/200
6/6 [==============================] - 1s 95ms/step - loss: 1.3469 - accuracy: 0.4494
Epoch 116/200
6/6 [==============================] - 1s 103ms/step - loss: 1.3426 - accuracy: 0.4719
Epoch 117/200
6/6 [==============================] - 1s 153ms/step - loss: 1.3164 - accuracy: 0.5112
Epoch 118/200
6/6 [==============================] - 1s 153ms/step - loss: 1.3283 - accuracy: 0.4607
Epoch 119/200
6/6 [==============================] - 1s 154ms/step - loss: 1.2929 - accuracy: 0.5337
Epoch 120/200
6/6 [==============================] - 1s 155ms/step - loss: 1.2865 - accuracy: 0.5112
Epoch 121/200
6/6 [==============================] - 1s 158ms/step - loss: 1.3387 - accuracy: 0.4438
Epoch 122/200
6/6 [==============================] - 1s 159ms/step - loss: 1.3154 - accuracy: 0.5169
Epoch 123/200
6/6 [==============================] - 1s 164ms/step - loss: 1.2630 - accuracy: 0.5225
Epoch 124/200
6/6 [==============================] - 1s 100ms/step - loss: 1.2408 - accuracy: 0.5843
Epoch 125/200
6/6 [==============================] - 1s 95ms/step - loss: 1.2203 - accuracy: 0.5787
Epoch 126/200
6/6 [==============================] - 1s 95ms/step - loss: 1.2080 - accuracy: 0.6124
Epoch 127/200
6/6 [==============================] - 1s 97ms/step - loss: 1.1882 - accuracy: 0.6067
Epoch 128/200
6/6 [==============================] - 1s 96ms/step - loss: 1.1752 - accuracy: 0.5730
Epoch 129/200
6/6 [==============================] - 1s 101ms/step - loss: 1.1441 - accuracy: 0.6517
Epoch 130/200
6/6 [==============================] - 1s 94ms/step - loss: 1.1083 - accuracy: 0.6685
Epoch 131/200
6/6 [==============================] - 1s 103ms/step - loss: 1.1449 - accuracy: 0.5899
Epoch 132/200
6/6 [==============================] - 1s 103ms/step - loss: 1.1228 - accuracy: 0.6180
Epoch 133/200
6/6 [==============================] - 1s 96ms/step - loss: 1.1138 - accuracy: 0.6517
Epoch 134/200
6/6 [==============================] - 1s 96ms/step - loss: 1.0955 - accuracy: 0.6292
Epoch 135/200
6/6 [==============================] - 1s 95ms/step - loss: 1.0808 - accuracy: 0.6798
Epoch 136/200
6/6 [==============================] - 1s 98ms/step - loss: 1.0624 - accuracy: 0.6910
Epoch 137/200
6/6 [==============================] - 1s 95ms/step - loss: 1.0769 - accuracy: 0.6742
Epoch 138/200
6/6 [==============================] - 1s 99ms/step - loss: 1.0980 - accuracy: 0.6517
Epoch 139/200
6/6 [==============================] - 1s 98ms/step - loss: 1.0786 - accuracy: 0.6292
Epoch 140/200
6/6 [==============================] - 1s 95ms/step - loss: 1.1146 - accuracy: 0.5730
Epoch 141/200
6/6 [==============================] - 1s 150ms/step - loss: 1.1083 - accuracy: 0.6404
Epoch 142/200
6/6 [==============================] - 1s 150ms/step - loss: 1.1186 - accuracy: 0.6011
Epoch 143/200
6/6 [==============================] - 1s 153ms/step - loss: 1.1886 - accuracy: 0.5393
Epoch 144/200
6/6 [==============================] - 1s 152ms/step - loss: 1.1498 - accuracy: 0.5618
Epoch 145/200
6/6 [==============================] - 1s 163ms/step - loss: 1.1328 - accuracy: 0.5730
Epoch 146/200
6/6 [==============================] - 1s 162ms/step - loss: 1.0761 - accuracy: 0.6292
Epoch 147/200
6/6 [==============================] - 1s 157ms/step - loss: 1.0399 - accuracy: 0.6685
Epoch 148/200
6/6 [==============================] - 1s 123ms/step - loss: 1.0482 - accuracy: 0.6404
Epoch 149/200
6/6 [==============================] - 1s 98ms/step - loss: 1.0935 - accuracy: 0.5899
Epoch 150/200
6/6 [==============================] - 1s 96ms/step - loss: 1.0836 - accuracy: 0.5787
Epoch 151/200
6/6 [==============================] - 1s 97ms/step - loss: 1.0584 - accuracy: 0.6573
Epoch 152/200
6/6 [==============================] - 1s 93ms/step - loss: 1.0787 - accuracy: 0.6067
Epoch 153/200
6/6 [==============================] - 1s 97ms/step - loss: 1.0784 - accuracy: 0.6067
Epoch 154/200
6/6 [==============================] - 1s 96ms/step - loss: 1.1129 - accuracy: 0.5393
Epoch 155/200
6/6 [==============================] - 1s 97ms/step - loss: 1.0884 - accuracy: 0.6124
Epoch 156/200
6/6 [==============================] - 1s 98ms/step - loss: 1.0724 - accuracy: 0.5955
Epoch 157/200
6/6 [==============================] - 1s 96ms/step - loss: 1.0388 - accuracy: 0.6404
Epoch 158/200
6/6 [==============================] - 1s 102ms/step - loss: 0.9992 - accuracy: 0.7135
Epoch 159/200
6/6 [==============================] - 1s 95ms/step - loss: 0.9498 - accuracy: 0.7528
Epoch 160/200
6/6 [==============================] - 1s 97ms/step - loss: 0.9396 - accuracy: 0.7584
Epoch 161/200
6/6 [==============================] - 1s 94ms/step - loss: 0.9428 - accuracy: 0.7135
Epoch 162/200
6/6 [==============================] - 1s 97ms/step - loss: 0.9430 - accuracy: 0.6348
Epoch 163/200
6/6 [==============================] - 1s 97ms/step - loss: 0.8967 - accuracy: 0.7697
Epoch 164/200
6/6 [==============================] - 1s 130ms/step - loss: 0.8656 - accuracy: 0.8090
Epoch 165/200
6/6 [==============================] - 1s 247ms/step - loss: 0.8884 - accuracy: 0.7809
Epoch 166/200
6/6 [==============================] - 1s 155ms/step - loss: 1.6586 - accuracy: 0.4607
Epoch 167/200
6/6 [==============================] - 1s 153ms/step - loss: 1.5373 - accuracy: 0.4270
Epoch 168/200
6/6 [==============================] - 1s 154ms/step - loss: 1.3898 - accuracy: 0.4438
Epoch 169/200
6/6 [==============================] - 1s 159ms/step - loss: 1.3184 - accuracy: 0.4551
Epoch 170/200
6/6 [==============================] - 1s 162ms/step - loss: 1.2078 - accuracy: 0.5169
Epoch 171/200
6/6 [==============================] - 1s 161ms/step - loss: 1.1528 - accuracy: 0.5787
Epoch 172/200
6/6 [==============================] - 1s 98ms/step - loss: 1.1330 - accuracy: 0.5112
Epoch 173/200
6/6 [==============================] - 1s 96ms/step - loss: 1.0633 - accuracy: 0.6180
Epoch 174/200
6/6 [==============================] - 1s 97ms/step - loss: 1.0203 - accuracy: 0.6404
Epoch 175/200
6/6 [==============================] - 1s 94ms/step - loss: 1.0061 - accuracy: 0.6742
Epoch 176/200
6/6 [==============================] - 1s 99ms/step - loss: 0.9665 - accuracy: 0.7360
Epoch 177/200
6/6 [==============================] - 1s 98ms/step - loss: 1.0031 - accuracy: 0.6966
Epoch 178/200
6/6 [==============================] - 1s 94ms/step - loss: 0.9758 - accuracy: 0.6910
Epoch 179/200
6/6 [==============================] - 1s 98ms/step - loss: 0.9628 - accuracy: 0.7135
Epoch 180/200
6/6 [==============================] - 1s 95ms/step - loss: 1.0082 - accuracy: 0.6629
Epoch 181/200
6/6 [==============================] - 1s 97ms/step - loss: 0.9403 - accuracy: 0.6854
Epoch 182/200
6/6 [==============================] - 1s 99ms/step - loss: 0.9263 - accuracy: 0.6966
Epoch 183/200
6/6 [==============================] - 1s 93ms/step - loss: 0.8792 - accuracy: 0.7472
Epoch 184/200
6/6 [==============================] - 1s 98ms/step - loss: 0.8727 - accuracy: 0.7472
Epoch 185/200
6/6 [==============================] - 1s 93ms/step - loss: 0.8397 - accuracy: 0.7809
Epoch 186/200
6/6 [==============================] - 1s 99ms/step - loss: 0.8179 - accuracy: 0.7753
Epoch 187/200
6/6 [==============================] - 1s 97ms/step - loss: 0.8242 - accuracy: 0.7865
Epoch 188/200
6/6 [==============================] - 1s 100ms/step - loss: 0.8288 - accuracy: 0.7640
Epoch 189/200
6/6 [==============================] - 1s 156ms/step - loss: 0.7935 - accuracy: 0.8146
Epoch 190/200
6/6 [==============================] - 1s 152ms/step - loss: 0.8026 - accuracy: 0.7247
Epoch 191/200
6/6 [==============================] - 1s 157ms/step - loss: 0.7873 - accuracy: 0.7753
Epoch 192/200
6/6 [==============================] - 1s 152ms/step - loss: 0.7984 - accuracy: 0.7640
Epoch 193/200
6/6 [==============================] - 1s 161ms/step - loss: 0.7664 - accuracy: 0.7921
Epoch 194/200
6/6 [==============================] - 1s 155ms/step - loss: 0.7572 - accuracy: 0.7809
Epoch 195/200
6/6 [==============================] - 1s 160ms/step - loss: 0.7338 - accuracy: 0.8371
Epoch 196/200
6/6 [==============================] - 1s 126ms/step - loss: 0.7560 - accuracy: 0.8090
Epoch 197/200
6/6 [==============================] - 1s 95ms/step - loss: 0.7382 - accuracy: 0.7753
Epoch 198/200
6/6 [==============================] - 1s 96ms/step - loss: 0.7119 - accuracy: 0.8371
Epoch 199/200
6/6 [==============================] - 1s 94ms/step - loss: 0.7229 - accuracy: 0.8371
Epoch 200/200
6/6 [==============================] - 1s 95ms/step - loss: 0.6854 - accuracy: 0.8483
<keras.callbacks.History at 0x7a8418452f50>
woods these are i think i know his house is in the village though he will not see me stopping here to watch his woods fill up with snow my little horse must think it queer to stop without a farmhouse near between the woods and frozen lake the darkest evening
Generated Text:
of the year he gives his harness bells a shake to ask there is some some mistake only other other sounds the sweep of easy wind and downy flake the woods are lovely dark and deep but i have promises to keep and miles to go before i go go go i i i i sleep admiring light on a sunny day erika fitzpatrick what light this is i may it know its beams barred by finite time though he should not mind me pausing now to admire this light it it go my mind considers considers how there is