{

"cells": [

{

"cell_type": "code",

"execution_count": 1,

"metadata": {},

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{

"name": "stderr",

"output_type": "stream",

"text": [

"Using TensorFlow backend.\n",

"c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",

"c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",

"c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",

"c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",

"c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",

"c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",

"c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",

"c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",

"c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",

"c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",

"c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",

"c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",

" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"

]

},

{

"name": "stdout",

"output_type": "stream",

"text": [

"Downloading data from https://s3.amazonaws.com/text-datasets/nietzsche.txt\n",

"606208/600901 [==============================] - 0s 1us/step\n",

"corpus length: 600893\n",

"total chars: 57\n",

"nb sequences: 200285\n",

"Vectorization...\n"

]

},

{

"name": "stderr",

"output_type": "stream",

"text": [

"WARNING: Logging before flag parsing goes to stderr.\n",

"W0926 21:19:30.765121 9788 deprecation_wrapper.py:119] From c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",

"\n"

]

},

{

"name": "stdout",

"output_type": "stream",

"text": [

"Build model...\n"

]

},

{

"name": "stderr",

"output_type": "stream",

"text": [

"W0926 21:19:31.060807 9788 deprecation_wrapper.py:119] From c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",

"\n",

"W0926 21:19:31.137966 9788 deprecation_wrapper.py:119] From c:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",

"\n"

]

},

{

"ename": "TypeError",

"evalue": "Unexpected keyword argument passed to optimizer: learning_rate",

"output_type": "error",

"traceback": [

"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",

"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",

"\u001b[1;32m<ipython-input-1-f60c43a8adc2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 48\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDense\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchars\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'softmax'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 50\u001b[1;33m \u001b[0moptimizer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRMSprop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.01\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 51\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'categorical_crossentropy'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",

"\u001b[1;32mc:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\keras\\optimizers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, lr, rho, epsilon, decay, **kwargs)\u001b[0m\n\u001b[0;32m 241\u001b[0m def __init__(self, lr=0.001, rho=0.9, epsilon=None, decay=0.,\n\u001b[0;32m 242\u001b[0m **kwargs):\n\u001b[1;32m--> 243\u001b[1;33m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRMSprop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 244\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 245\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'lr'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",

"\u001b[1;32mc:\\users\\david\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\keras\\optimizers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mallowed_kwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m raise TypeError('Unexpected keyword argument '\n\u001b[1;32m---> 79\u001b[1;33m 'passed to optimizer: ' + str(k))\n\u001b[0m\u001b[0;32m 80\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 81\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",

"\u001b[1;31mTypeError\u001b[0m: Unexpected keyword argument passed to optimizer: learning_rate"

]

}

],

"source": [

"from __future__ import print_function\n",

"from keras.callbacks import LambdaCallback\n",

"from keras.models import Sequential\n",

"from keras.layers import Dense\n",

"from keras.layers import LSTM\n",

"from keras.optimizers import RMSprop\n",

"from keras.utils.data_utils import get_file\n",

"import numpy as np\n",

"import random\n",

"import sys\n",

"import io\n",

"\n",

"path = get_file(\n",

" 'nietzsche.txt',\n",

" origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')\n",

"with io.open(path, encoding='utf-8') as f:\n",

" text = f.read().lower()\n",

"print('corpus length:', len(text))\n",

"\n",

"chars = sorted(list(set(text)))\n",

"print('total chars:', len(chars))\n",

"char_indices = dict((c, i) for i, c in enumerate(chars))\n",

"indices_char = dict((i, c) for i, c in enumerate(chars))\n",

"\n",

"# cut the text in semi-redundant sequences of maxlen characters\n",

"maxlen = 40\n",

"step = 3\n",

"sentences = []\n",

"next_chars = []\n",

"for i in range(0, len(text) - maxlen, step):\n",

" sentences.append(text[i: i + maxlen])\n",

" next_chars.append(text[i + maxlen])\n",

"print('nb sequences:', len(sentences))\n",

"\n",

"print('Vectorization...')\n",

"x = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)\n",

"y = np.zeros((len(sentences), len(chars)), dtype=np.bool)\n",

"for i, sentence in enumerate(sentences):\n",

" for t, char in enumerate(sentence):\n",

" x[i, t, char_indices[char]] = 1\n",

" y[i, char_indices[next_chars[i]]] = 1\n",

"\n",

"\n",

"# build the model: a single LSTM\n",

"print('Build model...')\n",

"model = Sequential()\n",

"model.add(LSTM(128, input_shape=(maxlen, len(chars))))\n",

"model.add(Dense(len(chars), activation='softmax'))\n",

"\n",

"optimizer = RMSprop(learning_rate=0.01)\n",

"model.compile(loss='categorical_crossentropy', optimizer=optimizer)\n",

"\n",

"\n",

"def sample(preds, temperature=1.0):\n",

" # helper function to sample an index from a probability array\n",

" preds = np.asarray(preds).astype('float64')\n",

" preds = np.log(preds) / temperature\n",

" exp_preds = np.exp(preds)\n",

" preds = exp_preds / np.sum(exp_preds)\n",

" probas = np.random.multinomial(1, preds, 1)\n",

" return np.argmax(probas)\n",

"\n",

"\n",

"def on_epoch_end(epoch, _):\n",

" # Function invoked at end of each epoch. Prints generated text.\n",

" print()\n",

" print('----- Generating text after Epoch: %d' % epoch)\n",

"\n",

" start_index = random.randint(0, len(text) - maxlen - 1)\n",

" for diversity in [0.2, 0.5, 1.0, 1.2]:\n",

" print('----- diversity:', diversity)\n",

"\n",

" generated = ''\n",

" sentence = text[start_index: start_index + maxlen]\n",

" generated += sentence\n",

" print('----- Generating with seed: \"' + sentence + '\"')\n",

" sys.stdout.write(generated)\n",

"\n",

" for i in range(400):\n",

" x_pred = np.zeros((1, maxlen, len(chars)))\n",

" for t, char in enumerate(sentence):\n",

" x_pred[0, t, char_indices[char]] = 1.\n",

"\n",

" preds = model.predict(x_pred, verbose=0)[0]\n",

" next_index = sample(preds, diversity)\n",

" next_char = indices_char[next_index]\n",

"\n",

" sentence = sentence[1:] + next_char\n",

"\n",

" sys.stdout.write(next_char)\n",

" sys.stdout.flush()\n",

" print()\n",

"\n",

"print_callback = LambdaCallback(on_epoch_end=on_epoch_end)\n",

"\n",

"model.fit(x, y,\n",

" batch_size=128,\n",

" epochs=60,\n",

" callbacks=[print_callback])"

]

},

{

"cell_type": "code",

"execution_count": null,

"metadata": {},

"outputs": [],

"source": []

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