From 6adb7253b5fe5c0cd572bb22afc0751f09094c4c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Geron?= Date: Tue, 31 Mar 2020 23:09:52 +1300 Subject: [PATCH] In TF 2.2.0-rc1, validation_data expects tuples, not lists, fixes #131 --- 14_deep_computer_vision_with_cnns.ipynb | 4 ++-- 17_autoencoders_and_gans.ipynb | 32 ++++++++++++------------- 2 files changed, 18 insertions(+), 18 deletions(-) diff --git a/14_deep_computer_vision_with_cnns.ipynb b/14_deep_computer_vision_with_cnns.ipynb index e2ca4c9..e22bde1 100644 --- a/14_deep_computer_vision_with_cnns.ipynb +++ b/14_deep_computer_vision_with_cnns.ipynb @@ -592,7 +592,7 @@ "outputs": [], "source": [ "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\", metrics=[\"accuracy\"])\n", - "history = model.fit(X_train, y_train, epochs=10, validation_data=[X_valid, y_valid])\n", + "history = model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))\n", "score = model.evaluate(X_test, y_test)\n", "X_new = X_test[:10] # pretend we have new images\n", "y_pred = model.predict(X_new)" @@ -1306,7 +1306,7 @@ "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\",\n", " metrics=[\"accuracy\"])\n", "\n", - "model.fit(X_train, y_train, epochs=10, validation_data=[X_valid, y_valid])\n", + "model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))\n", "model.evaluate(X_test, y_test)" ] }, diff --git a/17_autoencoders_and_gans.ipynb b/17_autoencoders_and_gans.ipynb index e345bc9..858fbb7 100644 --- a/17_autoencoders_and_gans.ipynb +++ b/17_autoencoders_and_gans.ipynb @@ -282,7 +282,7 @@ "stacked_ae.compile(loss=\"binary_crossentropy\",\n", " optimizer=keras.optimizers.SGD(lr=1.5), metrics=[rounded_accuracy])\n", "history = stacked_ae.fit(X_train, X_train, epochs=20,\n", - " validation_data=[X_valid, X_valid])" + " validation_data=(X_valid, X_valid))" ] }, { @@ -448,7 +448,7 @@ "tied_ae.compile(loss=\"binary_crossentropy\",\n", " optimizer=keras.optimizers.SGD(lr=1.5), metrics=[rounded_accuracy])\n", "history = tied_ae.fit(X_train, X_train, epochs=10,\n", - " validation_data=[X_valid, X_valid])" + " validation_data=(X_valid, X_valid))" ] }, { @@ -488,7 +488,7 @@ " autoencoder = keras.models.Sequential([encoder, decoder])\n", " autoencoder.compile(optimizer, loss, metrics=metrics)\n", " autoencoder.fit(X_train, X_train, epochs=n_epochs,\n", - " validation_data=[X_valid, X_valid])\n", + " validation_data=(X_valid, X_valid))\n", " return encoder, decoder, encoder(X_train), encoder(X_valid)" ] }, @@ -545,7 +545,7 @@ "stacked_ae_1_by_1.compile(loss=\"binary_crossentropy\",\n", " optimizer=keras.optimizers.SGD(lr=0.1), metrics=[rounded_accuracy])\n", "history = stacked_ae_1_by_1.fit(X_train, X_train, epochs=10,\n", - " validation_data=[X_valid, X_valid])" + " validation_data=(X_valid, X_valid))" ] }, { @@ -602,7 +602,7 @@ "conv_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.SGD(lr=1.0),\n", " metrics=[rounded_accuracy])\n", "history = conv_ae.fit(X_train, X_train, epochs=5,\n", - " validation_data=[X_valid, X_valid])" + " validation_data=(X_valid, X_valid))" ] }, { @@ -658,7 +658,7 @@ "metadata": {}, "outputs": [], "source": [ - "history = recurrent_ae.fit(X_train, X_train, epochs=10, validation_data=[X_valid, X_valid])" + "history = recurrent_ae.fit(X_train, X_train, epochs=10, validation_data=(X_valid, X_valid))" ] }, { @@ -709,7 +709,7 @@ "denoising_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.SGD(lr=1.0),\n", " metrics=[rounded_accuracy])\n", "history = denoising_ae.fit(X_train, X_train, epochs=10,\n", - " validation_data=[X_valid, X_valid])" + " validation_data=(X_valid, X_valid))" ] }, { @@ -757,7 +757,7 @@ "dropout_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.SGD(lr=1.0),\n", " metrics=[rounded_accuracy])\n", "history = dropout_ae.fit(X_train, X_train, epochs=10,\n", - " validation_data=[X_valid, X_valid])" + " validation_data=(X_valid, X_valid))" ] }, { @@ -811,7 +811,7 @@ "simple_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.SGD(lr=1.),\n", " metrics=[rounded_accuracy])\n", "history = simple_ae.fit(X_train, X_train, epochs=10,\n", - " validation_data=[X_valid, X_valid])" + " validation_data=(X_valid, X_valid))" ] }, { @@ -924,7 +924,7 @@ "sparse_l1_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.SGD(lr=1.0),\n", " metrics=[rounded_accuracy])\n", "history = sparse_l1_ae.fit(X_train, X_train, epochs=10,\n", - " validation_data=[X_valid, X_valid])" + " validation_data=(X_valid, X_valid))" ] }, { @@ -1020,7 +1020,7 @@ "sparse_kl_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.SGD(lr=1.0),\n", " metrics=[rounded_accuracy])\n", "history = sparse_kl_ae.fit(X_train, X_train, epochs=10,\n", - " validation_data=[X_valid, X_valid])" + " validation_data=(X_valid, X_valid))" ] }, { @@ -1100,7 +1100,7 @@ "variational_ae.add_loss(K.mean(latent_loss) / 784.)\n", "variational_ae.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[rounded_accuracy])\n", "history = variational_ae.fit(X_train, X_train, epochs=25, batch_size=128,\n", - " validation_data=[X_valid, X_valid])" + " validation_data=(X_valid, X_valid))" ] }, { @@ -1454,7 +1454,7 @@ "])\n", "classifier.compile(loss=\"sparse_categorical_crossentropy\", optimizer=keras.optimizers.SGD(lr=0.02),\n", " metrics=[\"accuracy\"])\n", - "history = classifier.fit(X_train_small, y_train_small, epochs=20, validation_data=[X_valid, y_valid])" + "history = classifier.fit(X_train_small, y_train_small, epochs=20, validation_data=(X_valid, y_valid))" ] }, { @@ -1498,7 +1498,7 @@ " optimizer=keras.optimizers.SGD(lr=0.02),\n", " metrics=[\"accuracy\"])\n", "history = pretrained_clf.fit(X_train_small, y_train_small, epochs=30,\n", - " validation_data=[X_valid, y_valid])" + " validation_data=(X_valid, y_valid))" ] }, { @@ -1514,7 +1514,7 @@ " optimizer=keras.optimizers.SGD(lr=0.02),\n", " metrics=[\"accuracy\"])\n", "history = pretrained_clf.fit(X_train_small, y_train_small, epochs=20,\n", - " validation_data=[X_valid, y_valid])" + " validation_data=(X_valid, y_valid))" ] }, { @@ -1548,7 +1548,7 @@ "hashing_ae.compile(loss=\"binary_crossentropy\", optimizer=keras.optimizers.SGD(lr=1.0),\n", " metrics=[rounded_accuracy])\n", "history = hashing_ae.fit(X_train, X_train, epochs=10,\n", - " validation_data=[X_valid, X_valid])" + " validation_data=(X_valid, X_valid))" ] }, {