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Update libraries to latest version, including TensorFlow 2.4.1 and Scikit-Learn 0.24.1

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Aurélien Geron 2021-02-16 15:04:34 +13:00
parent 198227f586
commit f86635b233
3 changed files with 39 additions and 11 deletions
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2
14_deep_computer_vision_with_cnns.ipynb
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@ -1366,7 +1366,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.6" "version": "3.7.9"
}, },
"nav_menu": {}, "nav_menu": {},
"toc": { "toc": {

5
15_processing_sequences_using_rnns_and_cnns.ipynb
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@ -1868,7 +1868,8 @@
" arpegio = tf.reshape(arpegio, [1, -1])\n", " arpegio = tf.reshape(arpegio, [1, -1])\n",
" for chord in range(length):\n", " for chord in range(length):\n",
" for note in range(4):\n", " for note in range(4):\n",
" next_note = model.predict_classes(arpegio)[:1, -1:]\n", " #next_note = model.predict_classes(arpegio)[:1, -1:]\n",
" next_note = np.argmax(model.predict(arpegio), axis=-1)[:1, -1:]\n",
" arpegio = tf.concat([arpegio, next_note], axis=1)\n", " arpegio = tf.concat([arpegio, next_note], axis=1)\n",
" arpegio = tf.where(arpegio == 0, arpegio, arpegio + min_note - 1)\n", " arpegio = tf.where(arpegio == 0, arpegio, arpegio + min_note - 1)\n",
" return tf.reshape(arpegio, shape=[-1, 4])" " return tf.reshape(arpegio, shape=[-1, 4])"
@ -2010,7 +2011,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.6" "version": "3.7.9"
}, },
"nav_menu": {}, "nav_menu": {},
"toc": { "toc": {

43
16_nlp_with_rnns_and_attention.ipynb
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@ -309,6 +309,20 @@
"## Creating and Training the Model" "## Creating and Training the Model"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Warning**: the following code may take up to 24 hours to run, depending on your hardware. If you use a GPU, it may take just 1 or 2 hours, or less."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**: the `GRU` class will only use the GPU (if you have one) when using the default values for the following arguments: `activation`, `recurrent_activation`, `recurrent_dropout`, `unroll`, `use_bias` and `reset_after`. This is why I commented out `recurrent_dropout=0.2` (compared to the book)."
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 18,
@ -317,9 +331,11 @@
"source": [ "source": [
"model = keras.models.Sequential([\n", "model = keras.models.Sequential([\n",
" keras.layers.GRU(128, return_sequences=True, input_shape=[None, max_id],\n", " keras.layers.GRU(128, return_sequences=True, input_shape=[None, max_id],\n",
" dropout=0.2, recurrent_dropout=0.2),\n", " #dropout=0.2, recurrent_dropout=0.2),\n",
" dropout=0.2),\n",
" keras.layers.GRU(128, return_sequences=True,\n", " keras.layers.GRU(128, return_sequences=True,\n",
" dropout=0.2, recurrent_dropout=0.2),\n", " #dropout=0.2, recurrent_dropout=0.2),\n",
" dropout=0.2),\n",
" keras.layers.TimeDistributed(keras.layers.Dense(max_id,\n", " keras.layers.TimeDistributed(keras.layers.Dense(max_id,\n",
" activation=\"softmax\"))\n", " activation=\"softmax\"))\n",
"])\n", "])\n",
@ -346,6 +362,13 @@
" return tf.one_hot(X, max_id)" " return tf.one_hot(X, max_id)"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Warning**: the `predict_classes()` method is deprecated. Instead, we must use `np.argmax(model.predict(X_new), axis=-1)`."
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": 20,
@ -353,7 +376,8 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"X_new = preprocess([\"How are yo\"])\n", "X_new = preprocess([\"How are yo\"])\n",
"Y_pred = model.predict_classes(X_new)\n", "#Y_pred = model.predict_classes(X_new)\n",
"Y_pred = np.argmax(model.predict(X_new), axis=-1)\n",
"tokenizer.sequences_to_texts(Y_pred + 1)[0][-1] # 1st sentence, last char" "tokenizer.sequences_to_texts(Y_pred + 1)[0][-1] # 1st sentence, last char"
] ]
}, },
@ -1785,7 +1809,8 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"ids = model.predict_classes(X_new)\n", "#ids = model.predict_classes(X_new)\n",
"ids = np.argmax(model.predict(X_new), axis=-1)\n",
"for date_str in ids_to_date_strs(ids):\n", "for date_str in ids_to_date_strs(ids):\n",
" print(date_str)" " print(date_str)"
] ]
@ -1819,7 +1844,8 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"ids = model.predict_classes(X_new)\n", "#ids = model.predict_classes(X_new)\n",
"ids = np.argmax(model.predict(X_new), axis=-1)\n",
"for date_str in ids_to_date_strs(ids):\n", "for date_str in ids_to_date_strs(ids):\n",
" print(date_str)" " print(date_str)"
] ]
@ -1847,7 +1873,8 @@
"\n", "\n",
"def convert_date_strs(date_strs):\n", "def convert_date_strs(date_strs):\n",
" X = prepare_date_strs_padded(date_strs)\n", " X = prepare_date_strs_padded(date_strs)\n",
" ids = model.predict_classes(X)\n", " #ids = model.predict_classes(X)\n",
" ids = np.argmax(model.predict(X), axis=-1)\n",
" return ids_to_date_strs(ids)" " return ids_to_date_strs(ids)"
] ]
}, },
@ -2226,7 +2253,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"**Warning**: due to a TF bug, this version only works using TensorFlow 2.2." "**Warning**: due to a TF bug, this version only works using TensorFlow 2.2 or above."
] ]
}, },
{ {
@ -2711,7 +2738,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.6" "version": "3.7.9"
}, },
"nav_menu": {}, "nav_menu": {},
"toc": { "toc": {