add conv sentiment classifier soln

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# Convolutional Sentiment Classifier

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In this notebook, we build a *convolutional* neural net to classify IMDB movie reviews by their sentiment.

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#### Load dependencies

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``` python

import keras

from keras.datasets import imdb

from keras.preprocessing.sequence import pad_sequences

from keras.models import Sequential

from keras.layers import Dense, Dropout, Embedding

from keras.layers import SpatialDropout1D, Conv1D, GlobalMaxPooling1D # new!

from keras.callbacks import ModelCheckpoint

import os

from sklearn.metrics import roc_auc_score

import matplotlib.pyplot as plt

%matplotlib inline

```

%% Output

    Using TensorFlow backend.

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#### Set hyperparameters

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``` python

# output directory name:

output_dir = 'model_output/conv'

# training:

epochs = 4

batch_size = 128

# vector-space embedding:

n_dim = 64

n_unique_words = 5000

max_review_length = 400

pad_type = trunc_type = 'pre'

drop_embed = 0.2 # new!

# convolutional layer architecture:

n_conv = 256 # filters, a.k.a. kernels

k_conv = 3 # kernel length

# dense layer architecture:

n_dense = 256

dropout = 0.2

```

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#### Load data

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``` python

(x_train, y_train), (x_valid, y_valid) = imdb.load_data(num_words=n_unique_words) # removed n_words_to_skip

```

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#### Preprocess data

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``` python

x_train = pad_sequences(x_train, maxlen=max_review_length, padding=pad_type, truncating=trunc_type, value=0)

x_valid = pad_sequences(x_valid, maxlen=max_review_length, padding=pad_type, truncating=trunc_type, value=0)

```

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#### Design neural network architecture

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``` python

model = Sequential()

model.add(Embedding(n_unique_words, n_dim, input_length=max_review_length))

model.add(SpatialDropout1D(drop_embed))

model.add(Conv1D(n_conv, k_conv, activation='relu'))

# model.add(Conv1D(n_conv, k_conv, activation='relu'))

model.add(GlobalMaxPooling1D())

model.add(Dense(n_dense, activation='relu'))

model.add(Dropout(dropout))

model.add(Dense(1, activation='sigmoid'))

```

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``` python

model.summary()

```

%% Output

    _________________________________________________________________

    Layer (type)                 Output Shape              Param #

    =================================================================

    embedding_1 (Embedding)      (None, 400, 64)           320000

    _________________________________________________________________

    spatial_dropout1d_1 (Spatial (None, 400, 64)           0

    _________________________________________________________________

    conv1d_1 (Conv1D)            (None, 398, 256)          49408

    _________________________________________________________________

    global_max_pooling1d_1 (Glob (None, 256)               0

    _________________________________________________________________

    dense_1 (Dense)              (None, 256)               65792

    _________________________________________________________________

    dropout_1 (Dropout)          (None, 256)               0

    _________________________________________________________________

    dense_2 (Dense)              (None, 1)                 257

    =================================================================

    Total params: 435,457

    Trainable params: 435,457

    Non-trainable params: 0

    _________________________________________________________________

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#### Configure model

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``` python

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

```

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``` python

modelcheckpoint = ModelCheckpoint(filepath=output_dir+"/weights.{epoch:02d}.hdf5")

if not os.path.exists(output_dir):

    os.makedirs(output_dir)

```

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#### Train!

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``` python

# 89.1% validation accuracy in epoch 2

# ...with second convolutional layer is essentially the same at 89.0%

model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_valid, y_valid), callbacks=[modelcheckpoint])

```

%% Output

    Train on 25000 samples, validate on 25000 samples

    Epoch 1/4

    25000/25000 [==============================] - 45s - loss: 0.4800 - acc: 0.7472 - val_loss: 0.2931 - val_acc: 0.8754

    Epoch 2/4

    25000/25000 [==============================] - 3s - loss: 0.2502 - acc: 0.8974 - val_loss: 0.2602 - val_acc: 0.8918

    Epoch 3/4

    25000/25000 [==============================] - 3s - loss: 0.1719 - acc: 0.9357 - val_loss: 0.2811 - val_acc: 0.8848

    Epoch 4/4

    25000/25000 [==============================] - 3s - loss: 0.1147 - acc: 0.9609 - val_loss: 0.3094 - val_acc: 0.8834

    <keras.callbacks.History at 0x7f99e7711f60>

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#### Evaluate

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``` python

model.load_weights(output_dir+"/weights.01.hdf5") # zero-indexed

```

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``` python

y_hat = model.predict_proba(x_valid)

```

%% Output

    24544/25000 [============================>.] - ETA: 0s

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``` python

plt.hist(y_hat)

_ = plt.axvline(x=0.5, color='orange')

```

%% Output

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``` python

"{:0.2f}".format(roc_auc_score(y_valid, y_hat)*100.0)

```

%% Output

    '95.97'

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``` python

```