From ce18693f2bd5b3ba0d5c122c719f0494417cf0ef Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Olivier=20Can=C3=A9vet?= <olivier.canevet@idiap.ch>
Date: Thu, 7 Sep 2017 15:16:27 +0200
Subject: [PATCH] [test_keras] Add file with demo on MNIST
---
bob/learn/tensorflow/test/test_keras.py | 112 ++++++++++++++++++++++++
1 file changed, 112 insertions(+)
create mode 100644 bob/learn/tensorflow/test/test_keras.py
diff --git a/bob/learn/tensorflow/test/test_keras.py b/bob/learn/tensorflow/test/test_keras.py
new file mode 100644
index 00000000..88ac1605
--- /dev/null
+++ b/bob/learn/tensorflow/test/test_keras.py
@@ -0,0 +1,112 @@
+#!/usr/bin/env python
+
+import pprint
+
+from tensorflow.contrib.keras.python.keras.models import Sequential
+from tensorflow.contrib.keras.python.keras.engine import InputLayer
+from tensorflow.contrib.keras.python.keras.layers import Dense
+from tensorflow.contrib.keras.python.keras.layers import LSTM
+from tensorflow.contrib.keras.python.keras.layers import Lambda
+from tensorflow.contrib.keras.python.keras.layers import Flatten
+from tensorflow.contrib.keras.python.keras.layers import Reshape
+
+from tensorflow.contrib.keras.python.keras.datasets import mnist
+from tensorflow.contrib.keras.python.keras.utils import np_utils
+
+from tensorflow.contrib.keras.python.keras.utils.layer_utils import print_summary
+
+method = "drop-first" # See if/elif block below for explanation
+
+n_epochs = 2
+n_hidden = 32 # Inside the LSTM cell
+n_drop_first = 2 # Number of first output to drop after LSTM
+
+# Load you training data.
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+print("Train data {}".format(X_train.shape))
+print("Train labels {}".format(y_train.shape))
+
+if method == "squares":
+ # Example where MNIST images are squares (28,28)
+ X_train = X_train.reshape(X_train.shape[0], 28, 28).astype("float32")
+ X_test = X_test.reshape(X_test.shape[0], 28, 28).astype("float32")
+
+ X_train = X_train / 255
+ X_test = X_test / 255
+
+ y_train = np_utils.to_categorical(y_train)
+ y_test = np_utils.to_categorical(y_test)
+
+ print("Train data for training {}".format(X_train.shape))
+
+ # LSTM
+ model = Sequential()
+ model.add(LSTM(n_hidden, input_shape=(28,28)))
+ model.add(Dense(10, activation="softmax"))
+
+elif method == "lines":
+ # Example where MNIST images are lines (1, 784)
+ X_train = X_train.reshape(X_train.shape[0], 1, 784).astype("float32")
+ X_test = X_test.reshape(X_test.shape[0], 1, 784).astype("float32")
+
+ X_train = X_train / 255
+ X_test = X_test / 255
+
+ y_train = np_utils.to_categorical(y_train)
+ y_test = np_utils.to_categorical(y_test)
+
+ print("Train data {}".format(X_train.shape))
+
+ # LSTM
+ model = Sequential()
+ model.add(LSTM(n_hidden, input_shape=(1, 784)))
+ model.add(Dense(10, activation="softmax"))
+
+elif method == "drop-first":
+ # Example where we drop first sequences, keep only the last ones
+ # and plug a fully connected layer after. Input images are
+ # squares, a 28-long sequence of 28 features
+ X_train = X_train.reshape(X_train.shape[0], 28, 28).astype("float32")
+ X_test = X_test.reshape(X_test.shape[0], 28, 28).astype("float32")
+
+ X_train = X_train / 255
+ X_test = X_test / 255
+
+ y_train = np_utils.to_categorical(y_train)
+ y_test = np_utils.to_categorical(y_test)
+
+ n_steps = X_train.shape[1]
+ n_features = X_train.shape[2]
+ n_classes = y_test.shape[1]
+ n_new_steps = n_steps - n_drop_first
+
+ print("n_steps {}".format(n_steps))
+ print("n_features {}".format(n_features))
+ print("n_classes {}".format(n_classes))
+
+ # LSTM
+ model = Sequential()
+ model.add(LSTM(n_hidden, input_shape=(28, 28), return_sequences=True))
+ model.add(Lambda(lambda x: x[:,n_drop_first:,:]))
+ model.add(Reshape((n_hidden*n_new_steps,), input_shape=(n_new_steps, n_hidden)))
+ model.add(Dense(n_classes, activation="softmax"))
+
+######################################################################
+
+print_summary(model)
+
+model.compile(optimizer="adam",
+ loss="categorical_crossentropy",
+ metrics=["accuracy"])
+
+model.fit(X_train, y_train,
+ epochs=n_epochs,
+ batch_size=32,
+ validation_data=(X_test, y_test))
+
+out = model.predict_on_batch(X_test[0:7, :])
+print(out.shape)
+
+# for layer in model.layers:
+# print("{} {}".format(layer.name, model.get_layer(layer.name).output.shape))
--
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